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An integrated GIS, MIF, and TOPSIS approach for appraising electric vehicle charging station suitability zones in Mumbai, India
Abstract
Fossil fuels cause air pollution and climate change, impacting human health. Mumbai imports and spends heavily on petroleum. Therefore, to reduce the amount of fossil fuel and for mitigating environmental issues, the use of electric vehicles (EVs) is an effective solution. The first priority towards supporting the widespread adoption of EVs is the availability of convenient charging stations. This research aims to delineate the optimal places for new electric vehicle charging stations (EVCS) in study area. The interrelationship of thirteen parameters have been used to determine the Multi Influencing Factor (MIF) weights. These MIF weights were then integrated into Geographical Information System (GIS) for weighted overlay analysis. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) used to assign ranks based on suitability index values. The result shows that the zone falling between 297.587 to 488.520 suitability index has suitability for EVCS. The proposed methodology offers a more precise solution for EVCS problems with a high level of uncertainty and assists policymakers and administrators in making effective decisions for future planning and strategies.
... Several studies highlight the promise of this GIS-MCDM fusion. For instance, Rane et al. (2023) in Mumbai combined GIS with multiinfluence factor (MIF) and TOPSIS, utilizing 13 criteria spanning transportation, societal aspects, energy, and environmental considerations. Similarly, Ghosh, Ghorui, Mondal, Kumari, Mondal, Das, and Gupta (2021) in Howrah, India, employed a hexagonal fuzzy MCDM model, covering facets like economics, environment, traffic, and societal factors. ...
... The resultant suitability maps for EVCS, delineated by random forest, AHP, fuzzy AHP, and SWARA, are vividly illustrated in Fig. 13, Fig. 14, Fig. 15, and Fig. 16, respectively. Many studies, including those by Guler and Yomralioglu (2018a, 2018b, 2020, Kaya et al. (2022Kaya et al. ( , 2021, , Kaya, Tortum, et al. (2020), Linzhao (2020) and Rane et al. (2023) harnessed GIS-based MCDM for pinpointing optimal zones for EVCS installation. Post-generation of their suitability maps, these areas were segmented into varying classes, ranging from minimal suitability to pinnacle suitability. ...
... Waste Management Facility Siting, including waste transfer stations, incinerators, and landfills, plays a significant role (Sambiani, Lare, Zanguina, & Narra, 2023;Uyan & Ertunç, 2023). Similarly, Shared Mobility Hub Allocation is pivotal in determining locations for shared mobility stations, integrating transit (Aydin, Seker, & Özkan, 2022;So, Chae, Hong, Youm, Kim, & Kim, 2023), bikeshare, scooters (Su, Yan, & Zhao, 2024), and EVCS (Rane et al., 2023). Additionally, Sustainable District Energy Planning, encompassing district heating, cooling, and electricity systems, is crucial for urban decarbonization (Eslami, Noorollahi, Marzband, & Anvari-Moghaddam, 2023). ...
Electric vehicles are emerging as sustainable transportation solutions worldwide. Inadequate electric vehicle charging stations (EVCS) hinder their broader adoption. Optimal EVCS site selection is vital, requiring multi-criteria decision-making (MCDM) analyses and geographic information systems (GIS). The research introduces, for the first time in site selection problems, an innovative methodology that integrates GIS, machine learning, and MCDM, effectively mapping the suitability of EVCS in urban environments. This study aims to fill the gap in evaluating EVCS placement in densely urbanized areas by adopting a retrospective approach to examine both primary and secondary criteria at existing EVCS sites. Focusing on Prague-a city with a dense EVCS network-it assesses their suitability using various MCDM techniques, representing a significant advance in optimizing EVCS distribution. Spatial analysis facilitated criteria reclassification, and the random forest (RF) algorithm identified key criteria, particularly transportation infrastructure and population density. Analytic hierarchy process (AHP), fuzzy AHP, and stepwise weight assessment ratio analysis (SWARA) are employed to derive criteria weights and suitability maps. Comparative results showed a predilection towards fuzzy AHP over other MCDM methods for modeling suitability analysis for placing EVCS, indicating its marginal effectiveness with the largest high-suitability area (172 km 2) and hosting the most EVCS (461) in this zone with the highest average score (4.49). This study not only assesses criteria importance and technique efficacy but also signifies a paradigm shift in MCDM from subjective to objective, data-driven decision-making by incorporating machine learning. The introduced approach offers guidance for EVCS planning and expansion by pinpointing areas that optimize service quality.
... The AUC metric is a single number that summarizes the overall performance of the ROC curve. Any model's validity is assessed by comparing its anticipated outcomes to a collection of facts from the actual world, looking at how well it achieves its objectives, and assuring the model is appropriateror (Poddar et al., 2023;Poddar et al., 2023;Rane et al., 2023). We validated the EBF and FR model ensemble using the receiver operating characteristics (ROC) curve. ...
... The AUC metric is a single number that summarizes the overall performance of the ROC curve. Any model's validity is assessed by comparing its anticipated outcomes to a collection of facts from the actual world, looking at how well it achieves its objectives, and assuring the model is appropriateror (Poddar et al., 2023;Poddar et al., 2023;Rane et al., 2023). We validated the EBF and FR model ensemble using the receiver operating characteristics (ROC) curve. ...
... The AUC metric is a single number that summarizes the overall performance of the ROC curve. Any model's validity is assessed by comparing its anticipated outcomes to a collection of facts from the actual world, looking at how well it achieves its objectives, and assuring the model is appropriateror (Poddar et al., 2023;Poddar et al., 2023;Rane et al., 2023). We validated the EBF and FR model ensemble using the receiver operating characteristics (ROC) curve. ...
Predicting landslides has become a critical global challenge for promoting sustainable development in mountainous regions. This study conducts a comparative analysis of landslide susceptibility maps (L.S.M.s) generated using two GIS-based data-driven bivariate statistical models: (a) Frequency Ratio (F.R.) and (b) Evidential Belief Function (E.B.F). These models are applied and evaluated in the high landslide-prone upper and middle Teesta basin of the Darjeeling-Sikkim Himalaya, leveraging geographic information system (GIS) and remote sensing techniques. We compile a comprehensive landslide inventory map containing 2387 regional landslide points. We use approximately 70% of this dataset for model training and reserve the remaining 30% for validation. In the construction of the Landslide Susceptibility maps (LSMs), a comprehensive set of twenty-one landslide-triggering parameters has been considered. These parameters encompass factors such as elevation, distance from drainage, distance from lineament, distance from roads, geology, geomorphology, lithology, land use, and land cover, normalized difference vegetation index, profile curvature, rainfall, relief amplitude, roughness, slope, slope aspect, slope classes, stream power index, sediment transport index, topographic position index, topographic ruggedness index, and topographic wetness index. An examination of multicollinearity statistics reveals no collinearity issues among the twenty-one causative factors utilized in this research. The final L.S.M.s demonstrate that the combined application of the F.R. and E.B.F. models yields the highest training accuracy at 98.10%. The insights derived from this study hold significant promise as valuable tools for assessing environmental hazards and land use planning.
... Numerous studies in different research fields have demonstrated the effectiveness of using MIF and GIS together to solve site selection issues (Etikala et al., 2019;Anbarasu et al., 2020;Singh et al., 2021a). The literature survey shows the lack of applications of MIF-based techniques that identify suitable site locations for the landfill development in municipality level, despite the fact that there have been numerous MIF-based studies conducted in the world in the areas of groundwater potential mapping (Rane and Jayaraj, 2022), food evaluation , appropriateness of the site suitability for urban development (Mallick et al., 2022), dam site suitability (Rane et al., 2023a), ideal location for electric vehicle charging station (Rane et al., 2023b), optimal sites for solar PV farms (Rane et al., 2024), evaluation of the alluvial aquifer , estimating the possibility of land deterioration (Mallick et al., 2022) and appropriate locations for water-saving technology (Shinde et al., 2022). It creates a nuanced layer to the decision-making process, ensuring a more precise and context-specific evaluation (Mallick, 2024). ...
... This step is most important because different criterion, interrelations are made from literature or by experts very cautiously. Expert opinion is the prerequisite in MCDM techniques (Rane et al., 2023a). Interactions between criteria are mainly two types; it may be major or may be minor criteria. ...
... The AHP is one among the MCDM methods which is been widely used in several research for installation of solar photovoltaic in suitable site [79]. The literature survey shows the lack of applications of MIF-based techniques that identify suitable site locations for the installation of solar PV, despite the fact that there have been numerous MIF-based studies conducted in the world in the areas of groundwater potential mapping [66], food evaluation [59], appropriateness of the site suitability for urban development [48], dam site suitability [63], ideal location for electric vehicle charging station [64], evaluation of the alluvial aquifer [86], estimating the possibility of land deterioration [48] and appropriate locations for watersaving technology [78]. The literature shows, there are limited studies with GIS-MIF integration in determining suitable site locations for PV solar plant installation [63]. ...
... The major factors were given a weight of 1, while the minor factors were given a weight of 0.5. The sum of all weights from each element determines the relative score ( W M i ) of a factor impacting solar PV power plant potential [64]. ...
The significant natural energy sources for reducing the global usage of fossil fuels are renewable energy (RE) sources. Solar energy is a crucial and reliable RE source. Site selection for solar photovoltaic (PV) farms is a crucial issue in terms of spatial planning and RE policies. This study adopts a Geographic Information System (GIS)-based Multi-Influencing Factor (MIF) technique to enhance the precision of identifying and delineating optimal locations for solar PV farms. The choice of GIS and MIF is motivated by their ability to integrate diverse influencing factors, facilitating a holistic analysis of spatial data. The selected influencing factors include solar radiation, wind speed, Land Surface Temperature (LST), relative humidity, vegetation, elevation, land use, Euclidean distance from roads, and aspect. The optimal sites of solar PV power plant delineated revealed that ‘very low’ suitability of site covering 4.866% of the study area, ‘low’ suitability of site 13.190%, ‘moderate’ suitability of site 31.640%, ‘good’ suitability of site 32.347%, and ‘very good’ suit- ability of site for solar PV power plant encompassing 17.957% of the study area. The sensitivity analysis results show that the solar radiation, relative humidity, and elevation are the most effective on the accuracy of the prediction. The validation of the results shows the accuracy of solar PV power plant prediction using MIF technique in the study area was 81.80%. The integration of GIS and MIF not only enhances the accuracy of site suitability assessment but also pro- vides a practical implementation strategy. This research offers valuable insights for renewable energy policymakers, urban planners, and other stakeholders seeking to identify and develop optimal locations for solar energy power farms in their respective regions.
... Zhao et al. [14] use the grey relation analysis (GRA) method to make group multi-criteria decisions for locating EV charging stations. The TOPSIS method is utilised in ref. [15] to assign ranks based on suitability index values for appraising EV charging station suitability zones in Mumbai, India. However, each of the above-mentioned methods has its limitations. ...
The rapid development of electric vehicles (EVs) has made the construction and service capability evaluation of EV charging and battery swapping stations (CBSSs) more important. A comprehensive evaluation indicator system for the service capability evaluation of EV‐CBSSs is established, meticulously outlining the critical aspects of operational efficiency, economy, convenience, and reliability, each with multiple indicators for thorough assessment. To address the shortcomings of individual evaluation methods in evaluating the service capability of EV‐CBSSs, the game theory‐based combination weighting (GTCW) method is adopted, which integrates the advantages of the analytic hierarchy process method, entropy weight method, and grey relation analysis method. Specifically, the weights for each indicator are obtained separately using these three evaluation methods, and then combined using the GTCW method to calculate the final weights. In case studies, the service capability for each EV‐CBSS is calculated and compared between these three individual methods and the proposed GTCW method. Simulation results validate that the proposed evaluation indicator system and GTCW method can offer a more comprehensive evaluation of the service capability for EV‐CBSSs, providing guiding suggestions for future construction plans.
... In the nancial sector, ChatGPT is reshaping customer service and nancial advisory services [41][42][43]. Integrated into banking apps and websites, ChatGPT provides instant customer support for a wide range of customer queries, including account balance inquiries, transaction history, and fund transfers. is instant and accurate assistance enhances customer satisfaction and loyalty [44,45]. ...
Generative Artificial Intelligence (AI) models like ChatGPT have become transformative tools across various industries, marking the advent of Industry 4.0, Industry 5.0, and Society 5.0. These AI systems have significantly improved communication and problem-solving abilities, boosting efficiency and productivity in elds ranging from customer service to healthcare and education. However, their widespread use presents challenges. Concerns about data privacy, biases in generated content, and potential e ects on employment patterns raise important questions about the responsible integration of these technologies. In Industry 4.0, ChatGPT and similar AI systems have enabled automation and streamlined data analysis, optimizing manufacturing processes and supply chain management. As Industry 5.0 combines human intuition with advanced technologies, these AI models facilitate collaborative, adaptive, and empathetic human-machine interactions, creating safer workplaces and enhancing overall productivity. Furthermore, within the context of Society 5.0, these AI tools are crucial in addressing societal issues like healthcare accessibility and educational disparities, providing tailored and accessible solutions to diverse communities. This research examines the evolving role of ChatGPT and comparable generative AI in the realms of Industry 4.0, Industry 5.0, and Society 5.0. It explores the challenges associated with their implementation, underscoring the necessity for ethical frameworks and regulatory policies. Simultaneously, it underscores the vast opportunities these technologies present, from stimulating innovation to promoting inclusivity, shaping a future where AI enhances human abilities and cultivates a harmonious coexistence between humans and intelligent machines.
ChatGPT; Artificial intelligence; Generative
arti cial intelligence; Industry 4.0; Industry 5.0; Society 5.0
... AI-driven tools can provide personalized support to online learners, making distance education more e ective and accessible. [18,[63][64][65][66][67] Collaboration Between Humans and AI e future of AI in education involves closer collaboration between humans and intelligent machines. is partnership can lead to more e ective teaching and learning experiences, with AI handling routine tasks and providing valuable insights. ...
... To decide the most prominent EVCS location, Wang et al. (2023) integrated hesitant fuzzy linguistic term sets and extended comparative linguistic expressions with symbolic translation approach. Rane et al. (2023) designed a combined model based on multi-influencing factors, GIS, and TOPSIS for evaluating candidate EVCS locations. Hisoglu et al. (2023) introduced a GIS-AHP model to select a suitable solar-supplied EVCS site. ...
The adverse effects of traditional vehicles on the environment increase the demand for clean vehicles, such as electric vehicles (EVs). The correct positioning of the charging points for such vehicles certainly promotes the acceptance and spread of EVs. Indeed, selecting optimal locations for electric vehicle charging stations (EVCS) is crucial for shaping a sustainable future. This study introduces an integrated methodology under a double hierarchy linguistic context with a criteria importance through an inter-criteria correlation (CRITIC) technique for experts' reliability determination, an attitudinal Cronbach's method for criteria weight estimation, and a novel multi-criteria technique considering the compromise ranking of alternatives from distance to ideal solution (CRADIS) formulation for optimal EVCS location selection. Based on the results, criteria such as service capability, ecological impact, land cost, and traffic density are the most crucial, with Manapparai, India, as the optimal location for a new EVCS construction. Further, a crucial finding is that the social dimension is substantial compared to the economy and environment dimensions for EVCS location selection. The novelty of the paper is that (i) uncertainty and expression of choices in natural language form for locations for EVCS are modeled effectively using a double hierarchy structure, (ii) experts' weights are obtained methodically by considering hesitation and interactions among experts, (iii) interdependencies among criteria and importance of experts are considered during criteria weight determination, and (iv) locations are ranked by not only considering criteria type but also resembles closely to the human-centric decision process. A detailed sensitivity analysis is further conducted to prove the proposed approach's effectiveness and stability. In the context of sustainable transportation, the work could contribute to the relevant literature through a powerful decision-making tool.
... Also, Andrejić and Pajić (2023) by integrating the Best-Worst Method with the Combined Compromise Solution (CoCoSo) presented a novel decision-support model, employed to refine the recruitment process within the transportation sector. Rane et al. (2023a) presented an efficient MIF-TOPSIS (Multi Influencing Factor-TOPSIS) technique to determine the interrelated weights to solve the problems of optimal location for electric vehicle charging stations. Also, in another study, Rane et al. (2023b) have used the MIF-TOPSIS technique to evaluate the potentiality of sites for dam construction. ...
The importance of determining the appropriate location for the construction of the main centers of a company is quite tangible, so much so that the senior managers of an organization are trying to find the appropriate decision-making methods in this regard. In discrete decision-making methods, we see the existence of a decision-making matrix containing information on different options compared to the problem evaluation criteria. In this paper, a new decision-making method is presented in a way that tries to evaluate options more accurately by considering the possibility of changing the criteria's desirability in different ranges. It is not always appropriate to increase\decrease a positive\negative criterion. Even in some situations, crossing a certain limit can reduce the desirability of the desired criterion. Therefore, we will introduce the concept of value alteration boundaries for evaluation criteria in the formation of a decision matrix, which tries to examine the decision problems more realistically. Also, in the examination of alternatives, with simultaneous attention to ideal, anti-ideal and average solutions, evaluation and ranking based on accumulation and skewness of alternatives are presented. The present paper has examined the validation of methods by examining several numerical examples and also evaluating the candidate locations for the construction of a central warehouse in Gohar Zamin Mining and Industrial Company. Also, sensitivity analysis includes checking with other existing multi-criteria decision-making methods, changes in the number of problem options and checking the stability of the method, changes in the weight of the criteria and changes in the value alteration boundaries. Analysis of results shows that the proposed methods are efficient and suitable for dealing with multi-criteria decision-making problems.
Graphical abstract
... The precise choice of materials plays a pivotal role in various sectors (Prieto et al., 2023;Pedro et al., 2023;Rane et al., 2023;Rane et al., 2023a;Patil and Rane, 2023;Rane et al., 2023b;Rane et al., 2023c). ChatGPT can aid in the selection process by furnishing information regarding the characteristics, expenses, and environmental repercussions of various materials (Oluleye et al., 2023;Stanev et al., 2021;Nguyen et al., 2021). ...
... This indicates a competitive relationship between public transit availability and EV adoption. These insights are crucial for urban planners and policymakers, especially in integrating EV infrastructure with public transportation networks to promote sustainable urban mobility [36]. ...
Amid the global shift towards sustainable development, this study addresses the burgeoning electric vehicle (EV) market and its infrastructure challenges, particularly the lag in public charging facility development. Focusing on Wuhan, it utilizes big data to analyze EV charging behavior’s spatiotemporal aspects and the urban environment’s influence on charging efficiency. Employing a random forest regression and multiscale geographically weighted regression (MGWR), the research elucidates the nonlinear interaction between urban infrastructure and charging station usage. Key findings include (1) a direct correlation between EV charging patterns and urban temporal factors, with notable price elasticity; (2) the predominant influence of commuting distance, supplemented by the availability of fast-charging options; and (3) a strategic proposal for increasing slow-charging facilities at key urban locations to balance operational costs and user demand. The study combines spatial analysis and charging behavior to recommend enhancements in public EV charging infrastructure layouts.
... The significance of ChatGPT in the realm of Ultra-High-Performance Concrete (UHPC) lies at the confluence of artificial intelligence, natural language processing, and materials science [72][73][74][75][76]. UHPC, a state-of-the-art construction material distinguished by its remarkable strength, durability, and versatility, stands to benefit profoundly from the integration of ChatGPT and AI [77][78][79][80][81][82]. This collaboration has far-reaching implications for the research, development, and application of this advanced construction material [83][84][85][86][87]. ...
This research paper investigates the transformative integration of ChatGPT, an advanced language model, across diverse aspects of concrete technology, reshaping the landscape of the construction industry. The study explores the versatile applications of ChatGPT in tackling challenges and elevating the performance of concrete materials in various domains. Examining the role of ChatGPT in advancing self-healing concrete, a pioneering strategy to mitigate cracks and deterioration in concrete structures, the paper highlights how the model's natural language processing capabilities contribute to the development of self-healing mechanisms, enhancing the longevity and durability of concrete infrastructures. Furthermore, the paper delves into the incorporation of ChatGPT in the domain of graphene-enhanced concrete, where it facilitates the synthesis and optimization of graphene-based materials, improving the mechanical and electrical properties of concrete. The investigation extends to the utilization of ChatGPT in innovative realms such as 3D/4D/5D/6D printing of concrete structures, ultra-high-performance concrete (UHPC), recycled and sustainable concrete, and smart concrete. The research also explores how ChatGPT plays a crucial role in the design and formulation of bioconcrete, leveraging its language understanding capabilities to integrate biological agents for enhanced sustainability and eco-friendliness. Additionally, ChatGPT contributes significantly to the development of alkali-activated binders, providing insights and recommendations for optimizing these binders in concrete applications. The impact of nanotechnology on concrete is scrutinized, with ChatGPT aiding in the analysis and formulation of nanomaterials to enhance strength and durability. The study emphasizes how ChatGPT facilitates collaboration among experts in the field, promoting knowledge exchange and fostering advancements in concrete technology. The integration of ChatGPT opens new avenues for collaboration and innovation, propelling the construction industry toward a sustainable and resilient future.
Keywords: Construction industry, Concrete, Compressive Strength, Forecasting, Reinforced Concrete, Cement, Aggregates, Sand.
... It can analyze existing policies, assess their effectiveness, and propose enhancements based on the latest scientific research and real-world data [68][69][70][71][72]. Additionally, ChatGPT can assist in stakeholder engagement by generating reports and summaries that communicate complex policy information in an accessible manner, fostering collaboration between governments, businesses, and civil society in implementing and adhering to policies [69,[73][74][75][76][77]. The development of innovative technologies is crucial for ambitious climate goals, and ChatGPT and AI can contribute by analyzing scientific literature, patents, and research papers to identify emerging technologies with significant climate impact [78][79][80][81][82]. ...
... In recent times, artificial intelligence (AI) has made notable advancements across various disciplines, with geotechnical engineering being no exception [87][88][89][90][91][92]. The state-of-the-art language model, ChatGPT, developed by OpenAI, has been instrumental in cutting-edge geotechnical research, transforming the approach to intricate problems [88,[93][94][95][96][97]. ...
The infusion of generative artificial intelligence (AI) stands out as a transformative influence in civil engineering, reshaping conventional methodologies and elevating the effectiveness and precision across various domains. This study delves into the nuanced impact of ChatGPT, a potent language model, in key realms within civil engineering: Structural Engineering, Geotechnical Engineering, Transportation Engineering, Environmental Engineering, Water Resources Engineering, Urban and Regional Planning, Materials Engineering, Coastal Engineering, and Earthquake Engineering. Within Structural Engineering, ChatGPT assumes a central role in formulating and refining structural designs. By deciphering intricate engineering concepts and proposing inventive solutions, ChatGPT assists engineers in crafting structures that not only exhibit resilience but also optimize resource utilization. Its proficiency in scrutinizing extensive datasets and delivering insights positions it as an invaluable tool for augmenting structural integrity and safety. Geotechnical Engineering benefits from ChatGPT's aptitude in processing and interpreting geological and geophysical data. Through the generation of reports and analyses, ChatGPT aids in recognizing potential risks and suggesting mitigation strategies, thereby expediting decision-making in geotechnical projects. In the realm of Transportation Engineering, the application of ChatGPT involves streamlining traffic flow, devising intelligent transportation systems, and refining overall infrastructure planning. Its natural language processing capabilities facilitate seamless communication and collaboration among diverse stakeholders engaged in transportation projects. In Environmental Engineering, ChatGPT contributes to the evaluation of environmental impact studies, assisting engineers and planners in making well-informed decisions prioritizing sustainability. Moreover, its capability to simulate environmental scenarios contributes to the formulation of effective pollution control measures. Water Resources Engineering leverages ChatGPT's role in data interpretation and modeling, enabling precise predictions of water flow patterns and aiding in the design of efficient water management systems. ChatGPT extends its contributions to Urban and Regional Planning, where it aids in formulating inventive urban development strategies, optimizing land use, and addressing challenges associated with population growth and urbanization. In Materials Engineering, Coastal Engineering, and Earthquake Engineering, ChatGPT's prowess in data analysis and generation contributes to the development of materials with enhanced properties, the design of resilient coastal structures, and the creation of earthquake-resistant infrastructure. This research paper scrutinizes how the integration of ChatGPT across these diverse civil engineering disciplines not only heightens the efficiency and precision of conventional practices but also unlocks new avenues for innovation, sustainability, and resilience in the face of evolving engineering challenges.
Keywords: ChatGPT, Bard, Structural Engineering, Geotechnical Engineering, Transportation Engineering, Environmental Engineering, Materials Engineering.
... By understanding design criteria and constraints, ChatGPT can generate design recommendations, provide rationale for design decisions, and assist in the optimization of foundation types, retaining structures, and slope stability measures [69][70][71][72][73]. Such collaborative approach can lead to more efficient and cost-effective designs [74][75][76][77][78]. ...
This research study investigates the influence of ChatGPT on geotechnical engineering and soil mechanics. Positioned as an advanced language model, ChatGPT has emerged as a formidable asset for enhancing communication, collaboration, and problem-solving within the geotechnics domain. A key aspect of ChatGPT's contribution lies in its capacity to facilitate streamlined communication among geotechnical engineers, researchers, and practitioners. Leveraging its natural language processing capabilities, the model enables seamless interaction, cultivating a collaborative environment for exchanging ideas, sharing insights, and addressing intricate challenges in geotechnical projects. This heightened communication leads to expedited problem resolution and enhanced decision-making processes. Additionally, ChatGPT plays a crucial role in automating routine tasks in geotechnical engineering, allowing professionals to allocate more time to critical analyses and innovative pursuits. The model's ability to generate human-like responses streamlines data interpretation, report generation, and documentation processes, thereby boosting overall workflow efficiency. In the context of slope stability analysis, ChatGPT showcases its prowess by assisting in the interpretation of geotechnical data and providing insights into potential failure mechanisms. Its application extends to soil mechanics, where the model aids in analyzing soil behaviour under varying conditions, contributing to a deeper understanding of soil-structure interaction. Moreover, ChatGPT proves invaluable in synthesizing and interpreting extensive datasets, paving the way for advancements in machine learning applications within geotechnical engineering. The model's aptitude for recognizing patterns and relationships within complex datasets enhances the accuracy of predictive models, ultimately elevating the reliability of geotechnical predictions. This paper delves into the integration of ChatGPT in geotechnical education, where the model serves as a valuable tool for interactive learning experiences. By generating realistic scenarios and providing instant feedback, ChatGPT contributes to the development of practical skills among students and professionals alike. The model's impact on communication, automation, analysis, and education establishes it as a pioneering force in shaping the future of geotechnical practices.
Keywords: ChatGPT, Bard, Artificial Intelligence, Geotechnical Engineering, Soil Mechanics, Soils, Forecasting.
... The construction industry traditionally heavily relies on steel and metal materials due to their strength, durability, and versatility [87][88][89][90]. However, the conventional production of these materials has significant environmental consequences, such as high energy consumption and greenhouse gas emissions [89,[91][92][93][94][95]. To address these challenges, innovative approaches and sustainable alternatives have emerged. ...
This study examines the potential transformation brought about by the integration of ChatGPT in advancing cutting-edge sustainable construction materials. Encompassing a diverse range of eco-friendly options, the investigation spans recycled materials, renewable resources, low-carbon concrete alternatives, energy-efficient materials, water-conserving compounds, green roofing materials, sustainable steel and metal, and lightweight construction materials. The utilization of recycled materials plays a pivotal role in sustainable construction, reducing environmental impact by repurposing discarded resources. Similarly, the incorporation of renewable materials aligns with sustainability principles, advocating for the use of resources that can naturally replenish. Low-carbon concrete alternatives address the carbon footprint associated with traditional concrete production, providing a more environmentally conscious choice in construction materials. The research explores energy-efficient materials that contribute to resource conservation and diminished energy consumption throughout buildings' lifecycle. Water-conserving materials are scrutinized for their potential in addressing water scarcity concerns, promoting responsible water usage in construction processes. Green roofing materials, renowned for insulation properties and environmental benefits, are studied for their role in sustainable construction practices. Additionally, the study examines sustainable steel and metal options, seeking alternatives with reduced environmental impact in production and usage. Lightweight construction materials are investigated for their potential to enhance energy efficiency and diminish transportation-related emissions. An integral aspect of this exploration involves evaluating how these materials collectively contribute to achieving Sustainable Development Goals (SDGs). The research investigates the multifaceted ways in which sustainable construction materials align with and propel these globally recognized goals. To guide the implementation of these advancements, the study proposes a comprehensive framework. This framework outlines strategies for integrating ChatGPT into research and development processes, leveraging artificial intelligence capabilities to enhance the efficiency and efficacy of sustainable construction material development. By merging technological innovation with sustainable practices, this research aims to drive the construction industry toward a more environmentally conscious and socially responsible future.
Keywords: ChatGPT, Bard, Construction Industry, Sustainable Development, Concrete, Natural language processing, Recycling
... A key stride in intelligent manufacturing involves seamless collaboration between humans and machines [73,[75][76][77][78][79]. ChatGPT plays a pivotal role in enhancing communication and interaction within manufacturing environments. ...
... The incorporation of ChatGPT into water pollution monitoring systems transforms it into a robust decision support tool [29,[72][73][74][75][76]. By interpreting complex data and providing insights in natural language, the model assists decision-makers in comprehending the implications of the data [73,[77][78][79][80][81]. This allows for more informed and timely decisions, including issuing advisories, implementing corrective measures, or allocating resources for pollution control efforts. ...
This research delves into the utilization of advanced artificial intelligence (AI), specifically ChatGPT or Bard, to improve strategies for monitoring and controlling water and air pollution. Given the escalating concerns surrounding environmental degradation and its repercussions on public health, there is a pressing demand for innovative pollution management techniques. This investigation centers on harnessing the capabilities of ChatGPT, an advanced language model, to address real-time data analysis, decision-making, and public engagement challenges within the realm of water and air quality. Incorporating cutting-edge methods in water and air pollution monitoring, such as sensor networks, satellite imagery, and IoT devices, this research aims to obtain a comprehensive understanding of pollution dynamics. Nevertheless, the substantial volume of data presents challenges in processing and extracting meaningful insights. ChatGPT is employed as an intelligent data processing tool proficient in comprehending natural language queries and delivering insightful analyses. This integration streamlines the interpretation of intricate data sets, enabling swift decision-making for pollution control authorities. Moreover, ChatGPT assumes a pivotal role in public engagement by serving as a user-friendly interface for disseminating information on pollution levels, regulatory measures, and preventive actions. Through interactive conversations, it enhances communication between environmental agencies and the general public, cultivating awareness and encouraging participation in pollution control initiatives. This paper underscores the significance of a collaborative human-AI approach in tackling the multifaceted challenges of water and air pollution. The research also delves into the ethical considerations and challenges associated with AI-driven pollution monitoring, emphasizing the importance of responsible AI implementation. As AI technologies progress, the proposed framework aims to contribute to the ongoing discourse on sustainable environmental management and public involvement. By synergizing the capabilities of ChatGPT with state-of-the-art monitoring techniques, this research seeks to offer a comprehensive and efficacious solution for advancing water and air pollution monitoring and control in the contemporary environmental landscape.
Keywords: ChatGPT, Bard, Pollution, Environmental Monitoring, Water Pollution, Air Pollution, Air Quality
... In India, the TOPSIS method was used to evaluate sites for EV charging stations based on the previously calculated weights, which were analysed using a geographical information system. The aim of the study was to help decision-makers in future planning and strategy development [25]. ...
Lithium-ion batteries (LIBs) have a wide range of applications in different fields, starting with electronics and energy storage systems. The potential of LIBs in the transportation sector is high, especially for electric vehicles (EVs). This study aims to investigate the efficiency and effectiveness of, and justification for, the application of LIBs in the field of transport, primarily in EVs. The research focuses on single and multi-criteria evaluations of the efficiency of LIBs. Previous studies in which LIBs were evaluated using cost–benefit analysis (CBA) and multi-criteria decision-making methods (MCDM) were analysed. An electronic literature search of the Web of Science, Scopus, and other relevant databases was performed. The literature was searched using the keywords: “lithium-ion batteries”; “multi-criteria decision-making”; “cost-benefit analysis”; “energy storage”; “vehicles”; “PROMETHEE” (or other MCDM method)”. A total of 40 scientific articles concerning the application of CBA (of which are 20%) and MCDM methods between 1997 and 2023, worldwide, were analysed. The results show multiple applications of both CBA and MCDM methods. The main findings of the areas of application were summarised and future research was discussed.
... A key application of metaverse technologies in education is simulation-based training [39,42]. Fields such as healthcare, aviation, and engineering can benefit greatly from realistic simulations [52][53][54][55][56]. Medical students, for example, can practice surgeries in a virtual environment, refining their skills without the risks associated with real-life procedures. ...
The incorporation of cutting-edge metaverse technologies into the field of education has emerged as a transformative paradigm, reshaping conventional learning environments and paving the way for novel immersive and interactive educational experiences. This paper undertakes a comprehensive examination of the framework, applications, challenges, and future development prospects of metaverse technologies within the educational domain, focusing on a diverse array of techniques that contribute to the continuous evolution of this innovative educational landscape. The framework analysis delves into the design and implementation strategies that form the foundation of metaverse-based educational platforms. Utilizing augmented reality (AR), virtual reality (VR), and mixed reality (MR), these frameworks strive to create dynamic and captivating learning environments that transcend geographical limitations. The paper explores how these technologies enable realistic simulations, collaborative problem-solving, and experiential learning, thereby fostering a more interactive and participatory educational experience. Case studies and examples illustrate their utilization across disciplines, spanning from science and mathematics to humanities and vocational training. The paper underscores the significance of artificial intelligence (AI) and machine learning (ML) algorithms in tailoring educational content to individual learning styles, thereby enhancing personalization and adaptability within metaverse-based educational systems. Nevertheless, the research also addresses the challenges associated with implementing metaverse technologies in education. Issues such as accessibility, digital equity, and data privacy are discussed, accompanied by considerations for overcoming these challenges to ensure a more inclusive and secure educational landscape. Looking ahead, the paper outlines potential developments in metaverse education, exploring emerging technologies and trends such as blockchain-based credentialing, haptic feedback integration, and the incorporation of the Internet of Things (IoT). The paper acknowledges the diverse techniques contributing to its ongoing evolution, emphasizing the dynamic nature of this transformative educational landscape.
Keywords: Metaverse, Education, Learning, Virtual reality, Augmented reality, E-learning, Students, Artificial intelligence.
... The precise choice of materials plays a pivotal role in various sectors (Prieto et al., 2023;Pedro et al., 2023;Rane et al., 2023;Rane et al., 2023a;Patil and Rane, 2023;Rane et al., 2023b;Rane et al., 2023c). ChatGPT can aid in the selection process by furnishing information regarding the characteristics, expenses, and environmental repercussions of various materials (Oluleye et al., 2023;Stanev et al., 2021;Nguyen et al., 2021). ...
This research paper delves into the integration of advanced generative artificial intelligence (AI) models, such as ChatGPT, Bard, and similar architectures, within the realms of architectural design and engineering. The comprehensive study explores various aspects, including applications, frameworks, challenges, and prospective developments in the context of architectural design and architectural engineering. In the domain of architectural design, the paper investigates the transformative impact on Architectural Theory, highlighting how generative AI fosters creativity and innovation in design thinking. The Design Process is scrutinized, showcasing how AI models streamline ideation, iteration, and collaboration among design teams. The role of generative AI in Representation and Visualization is explored, emphasizing its capacity to generate immersive and realistic visualizations. Furthermore, the research examines the influence of generative AI in Interior Design, Urban Design and Planning, and considers nuanced aspects of Cultural and Social factors, elucidating how these technologies contribute to inclusive and context-sensitive design practices. Within the realm of architectural engineering, the study assesses the integration of generative AI in Structural Engineering, demonstrating its potential to optimize and innovate structural analysis and designs for enhanced safety and efficiency. It explores applications in Building Systems and Construction Management, illustrating how AI can streamline project workflows and resource allocation. The impact of generative AI on compliance with Building Codes and Regulations is analyzed, emphasizing its potential for error reduction and adherence to standards. Additionally, the research probes into the influence of AI in Materials and Construction Technology, highlighting advancements in material selection and construction methodologies. The paper also investigates the role of generative AI in promoting Sustainability and Environmental Design, showcasing its potential to optimize energy efficiency, reduce environmental impact, and enhance overall sustainability. While presenting advancements and applications, the paper critically evaluates challenges posed by integrating generative AI in these domains, including ethical considerations, bias mitigation, and user adaptability. Finally, it outlines future directions for development, emphasizing the necessity for interdisciplinary collaboration, ethical guidelines, and ongoing research to fully harness the potential of generative AI in shaping the future of architectural design and engineering.
Keywords: ChatGPT, Bard, Large language models, Architectural design, Architectural engineering, Artificial intelligence, Construction industry, Building information modelling, Construction.
... For instance, the model can analyze sensor data to detect anomalies, monitor equipment health, and provide insights into environmental conditions that may affect safety. Such integration enhances the overall efficiency of site monitoring and protocols [120,[126][127][128][129][130]. Fig. 5 shows the implementation of ChatGPT in site monitoring and safety. ...
The infusion of generative artificial intelligence (AI), as exemplified by models such as ChatGPT and Bard is proving to be a revolutionary catalyst within the building and construction sector. This exploration delves into the myriad applications, establishes a conceptual framework, confronts challenges, and delineates the prospective trajectory of harnessing generative AI across diverse stages of the construction lifecycle. In the domain of project management and scheduling, AI models contribute to optimal resource allocation, task sequencing, and timeline optimization, thereby elevating the overall efficiency of project delivery. Design optimization is equally pivotal, as generative AI assists architects and engineers in crafting innovative designs that concurrently adhere to functional and aesthetic criteria. The predictive prowess of generative AI fortifies risk management, furnishing stakeholders with insights into potential project risks and effective mitigation strategies. Meanwhile, in the realm of cost estimation and budgeting, the enhanced accuracy and speed offered by generative AI optimize financial planning and resource allocation. Supply chain management benefits from streamlined processes driven by AI insights, ensuring the timely and cost-effective procurement of materials. Generative AI is a linchpin in quality control, identifying defects and deviations from standards to enhance overall construction quality. Real-time data analysis strengthens site monitoring and safety protocols, enabling proactive risk mitigation and ensuring a secure working environment. Collaboration and communication within construction teams are augmented by generative AI, facilitating seamless information exchange and decision-making processes. Predictive maintenance and asset management undergo a transformation, with AI algorithms predicting equipment failures and optimizing maintenance schedules. Furthermore, the integration of generative AI tackles the imperative of energy efficiency and sustainability in the construction sector. Models like ChatGPT and bard contribute significantly to optimizing building designs for energy conservation and sustainable practices. This paper also explores the incorporation of ChatGPT with augmented reality (AR), virtual reality (VR), and Building Information Modeling (BIM). Ethical concerns, data privacy, and the imperative for robust cybersecurity measures necessitate careful consideration. As the industry embraces these innovations, substantial improvements in efficiency, sustainability, and overall project outcomes are poised to unfold.
Keywords: ChatGPT, Bard, Generative artificial intelligence, Construction industry, Large language models, Project management, Artificial intelligence.
... From the integration of artificial intelligence to the exploration of sustainable materials and the advent of 3D printing, architects are presented with an unprecedented array of tools to innovate and redefine the built environment. -Ensuring accuracy and quality of AI-generated 3D models -Handling complex design geometries and details -Compatibility with existing 3D modeling tools and standards-User feedback and adjustments for AI-generated models Beyond the conceptual phase, AI and similar technology is increasingly employed in detailed design and documentation stages [75][76][77][78][79][80][81][82]. AI-powered automated drafting and documentation tools assist architects in the meticulous task of creating construction drawings. ...
In the dynamic field of architecture, the incorporation of state-of-the-art technologies is crucial for advancing design methodologies and achieving groundbreaking solutions. This exhaustive review delves into a diverse array of cutting-edge technologies reshaping the architectural design landscape. The scrutinized technologies not only amplify the efficiency of design processes but also contribute to the development of sustainable, adaptable, and technologically advanced built environments. Commencing with an exploration of Artificial Intelligence (AI) in architectural design, the paper underscores how machine learning algorithms and neural networks are revolutionizing the conceptualization and optimization of architectural forms. Emphasis is placed on AI's capacity to analyze extensive datasets, predict design trends, and generate alternative designs, thereby fostering creativity and streamlining the design process. The immersive potential of Virtual Reality (VR) and Augmented Reality (AR) in architecture is thoroughly examined. The paper elucidates how these technologies are not only transforming the visualization of designs but also facilitating collaborative design processes, enabling stakeholders to experience spaces before the commencement of construction. Parametric Design and Computational Modeling are scrutinized extensively, showcasing their pivotal role in crafting intricate and optimized structures. The exploration extends to Building Information Modeling (BIM), elucidating its significance in promoting collaboration, reducing errors, and streamlining the entire building lifecycle. Sustainable Building Materials and Technologies take center stage in the discussion, emphasizing the contribution of innovative materials and eco-friendly practices to environmentally conscious architectural designs. Generative Design is explored as a tool utilizing algorithms to explore numerous design iterations based on specified criteria, thereby promoting innovative and sustainable solutions. The paper further investigates ChatGPT, 3D/4D/5D/6D Printing and Responsive Architecture, highlighting their potential to revolutionize construction processes and create adaptive structures. Drones and Aerial Imaging are discussed for their roles in site analysis, surveying, and monitoring construction progress. Digital Twin Technology is underscored as a tool creating virtual replicas of physical buildings, enabling real-time monitoring and analysis. Smart Building Systems, integrating IoT technologies, are explored for their role in enhancing building performance, energy efficiency, and occupant comfort.
Keywords: Architectural design, Building Information Modelling, Construction Industry, Artificial Intelligence, Virtual Reality, Augmented Reality, Parametric Design, Computational Modeling, 3D printing
... Counterfactual explanations generate instances that, while similar to the original input, result in a different model prediction [104][105][106][107]. By showcasing what changes in the input would lead to an alternative outcome, counterfactual explanations help users understand the decision boundaries of the model [108][109][110][111][112]. In finance, this approach can be valuable for understanding how slight modifications to certain variables could impact investment decisions or risk assessments. ...
Recently, there has been a growing trend in incorporating Artificial Intelligence (AI) into financial decision-making, prompting concerns about the transparency and accountability of these intricate systems. This study investigates the impact of Explainable Artificial Intelligence (XAI) approaches in alleviating these concerns and improving transparency in financial decision-making processes. The paper commences by outlining the current landscape of AI applications in finance, underscoring the complex and opaque nature of advanced machine learning models. The lack of interpretability in these models presents a significant challenge, as stakeholders, regulators, and end-users often struggle to comprehend the reasoning behind AI-driven financial decisions. This opacity raises questions regarding accountability and trust, particularly in critical financial scenarios. The primary focus of the research centers on the analysis and implementation of XAI techniques to introduce transparency into financial AI systems. Various XAI methods, including rule-based systems, model-agnostic approaches, and interpretable machine learning models, are scrutinized for their effectiveness in producing understandable explanations for AI-driven financial decisions. The paper explores how these approaches can be tailored to meet the distinct requirements of the financial domain, where interpretability is essential for regulatory compliance and stakeholder confidence. Moreover, the research delves into the potential impact of XAI on accountability mechanisms within financial institutions. By offering interpretable explanations for model outputs, XAI not only enhances transparency but also empowers financial professionals to identify and rectify biases, errors, or unethical behaviour in AI algorithms. By promoting transparency and accountability, XAI not only addresses ethical concerns but also facilitates the responsible and trustworthy deployment of AI in the financial sector. This, in turn, contributes to the advancement of fair, reliable, and secure financial systems.
Keywords: Explainable Artificial Intelligence, Finance, Explainable AI, Risk assessment, Forecasting, Decision-making.
... In a connected health device, sensors monitor vital signs [35,39]. Such data acquisition is crucial for enabling intelligent decision-making and responsiveness [37,[121][122][123][124][125]. ...
This research paper offers a thorough examination of the incorporation of state-of-the-art technologies, such as Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), 3D Printing, Internet of Things (IoT), and Blockchain, with the aim of revolutionizing and elevating the product design and development process. As industries undergo transformation and seek inventive solutions, the convergence of these technologies emerges as a revolutionary strategy to streamline workflows, encourage collaboration, and achieve unprecedented efficiencies. The utilization of AI in product design is investigated as a pivotal catalyst, enabling the automated analysis of extensive datasets to extract valuable insights. AI algorithms play a crucial role in intelligent decision-making, predicting design trends, optimizing parameters, and facilitating swift prototyping. Virtual Reality and Augmented Reality technologies are employed to construct immersive design environments, enabling designers to interact with virtual prototypes in real-time and gain valuable perspectives prior to physical production. The paper explores the impact of 4D/5D/6D Printing in converting conceptual designs into tangible prototypes, expediting the iterative design process, and reducing time-to-market. Furthermore, the Internet of Things (IoT) is scrutinized for its potential to enhance product functionality by incorporating smart sensors and connectivity, allowing real-time monitoring, and facilitating data-driven design improvements throughout the product lifecycle. Additionally, the integration of Blockchain technology is examined for its ability to establish transparent and secure collaboration frameworks. Blockchain ensures the traceability of design iterations, safeguards intellectual property, and cultivates trust among stakeholders in a decentralized ecosystem. The cohesive integration of these technologies is presented as a comprehensive approach to redefine traditional product design and development paradigms. The paper concludes by outlining the future prospects of this integrated approach, emphasizing the necessity for ongoing research and development to unlock the full potential of these technologies in fostering innovation and reshaping the landscape of product design and development.
Keywords: Product design, 3D Printing, Artificial Intelligence, Virtual reality, Additives, Internet Of Things, Computer Aided Design
... This unified profile serves as the canvas upon which hyper-personalized strategies are crafted. Advanced customer segmentation tools refine this canvas, allowing organizations to categorize customers based on intricate criteria such as behavior, preferences, and demographics [15,[66][67][68][69][70]. Through dynamic segmentation, CRM systems can adapt in real-time, ensuring that the personalized experiences delivered remain relevant and resonant. ...
In the dynamic realm of customer-centric business models, organizations are increasingly adopting advanced technologies and strategies to augment the capabilities of Customer Relationship Management (CRM) systems. This study delves into the transformative concept of hyper-personalization and its profound impact on enhancing customer loyalty and satisfaction within CRM frameworks. Key enablers of hyper-personalization are artificial intelligence (AI) and machine learning (ML), which play a pivotal role in this evolution. These technologies empower CRM systems to analyse extensive datasets, extracting valuable insights into individual customer behaviours, preferences, and needs. The incorporation of predictive analytics and recommendation engines allows real-time customization of interactions, ensuring customers receive personalized content, product recommendations, and communication channels tailored to their unique profiles. This paper explores the tools integral to hyper-personalization strategies, underscoring the importance of data-driven decision-making. Customer data platforms (CDPs) are highlighted as essential tools that unify disparate data sources to create a comprehensive customer profile. Additionally, advanced customer segmentation tools assist in categorizing customers based on diverse criteria, facilitating targeted and personalized interactions. The integration of these tools enhances the ability of CRM systems to deliver contextually relevant experiences, fostering stronger emotional connections between customers and brands. The strategies for hyper-personalization involve a multi-faceted approach, encompassing proactive communication, personalized marketing campaigns, and adaptive customer journeys. The study evaluates the effectiveness of real-time personalization, where CRM systems dynamically adjust content and offers based on customer interactions, ensuring a seamless and relevant experience. Furthermore, the paper examines the role of omnichannel strategies in hyper-personalization, exploring how the integration of various touchpoints contributes to a cohesive and personalized customer journey. By embracing hyper-personalization, organizations can cultivate enduring customer relationships, ultimately driving enhanced loyalty and satisfaction in today's dynamic and competitive business landscape.
Keywords: Customer Relationship Management, Hyper-personalization, Customer loyalty, CRM, Customer satisfaction, personalization, Business, Customer experience.
... AI models can learn to recommend the most informative locations for soil sampling, considering factors like cost and uncertainty. Such adaptive approach minimizes required samples while maximizing information quality [53][54][55][56][57]. ...
Geotechnical site characterization is a crucial factor in the effective planning, design, and implementation of civil engineering projects. In the evolving landscape of infrastructure development, the integration of advanced technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) has emerged as a transformative strategy to improve the precision and efficiency of geotechnical site characterization processes. This article delves into the combined application of AI and IoT in geotechnical site characterization, encompassing a diverse range of technologies, models, tools, and frameworks. AI, utilizing its machine learning algorithms, has the capacity to analyse extensive geospatial and geological data, facilitating more accurate identification of subsurface conditions. Neural networks and deep learning models play a role in examining geological features, predicting soil behaviour, and evaluating potential risks associated with construction projects. In conjunction with AI, the incorporation of IoT technologies enables real-time monitoring and data acquisition at geotechnical sites. Ground-embedded sensor networks gather geophysical data, including soil moisture, temperature, and pressure, providing a dynamic and continuous understanding of subsurface conditions. This real-time data feeds into AI models, creating a feedback loop that refines predictions and enhances the precision of site characterization. Moreover, the article introduces various tools and frameworks that facilitate the seamless integration of AI and IoT in geotechnical engineering. Geographic Information Systems (GIS) are employed for spatial analysis, aiding in the visualization and interpretation of complex geological data. Additionally, Building Information Modelling (BIM) is explored as a means to integrate geotechnical information with overall project design, promoting a holistic approach to construction planning. Embracing this technological synergy is essential for addressing the challenges of modern infrastructure development and ensuring the sustainability and resilience of civil engineering projects in the future.
Keywords: Artificial Intelligence, Internet of Things, Geotechnical engineering, Soil mechanics, Site characterization, Soils, Soil testing.
... For instance, the introduction of ether-functionalized electrolytes has been explored to enhance the low-temperature performance of lithium-ion batteries [118][119][120][121]. Such tailored approaches aim to address specific challenges associated with traditional materials without completely replacing them [114,119,[122][123][124][125][126]. Table 1 shows the enhancement of lithium-ion battery performance with emerging electrolyte materials for sustainable energy storage solutions. ...
The swift expansion of renewable energy sources and the growing demand for electric vehicles have spurred intensive research into advancing energy storage technologies, with a primary focus on lithium-ion batteries (LIBs). This all-encompassing examination delves into the possibilities offered by emerging electrolyte materials to elevate LIB performance, tackling key obstacles and offering insights into sustainable energy storage solutions. The analysis provides a thorough exploration of recent progress in electrolyte materials and their impact on LIBs, shedding light on their electrochemical properties, safety considerations, and scalability. The review delves into the most recent innovations in electrolyte formulations, encompassing ionic liquids, solid-state electrolytes, and gel polymer electrolytes, each exhibiting promising attributes such as heightened thermal stability, enhanced safety profiles, and increased energy density. The incorporation of these novel materials has the potential to address longstanding issues associated with conventional liquid electrolytes, including flammability and limited cycle life. Various pertinent technologies are discussed within the context of electrolyte advancements. Notable breakthroughs involve the use of ionic liquid-based electrolytes to improve thermal stability and safety, solid-state electrolytes to eliminate flammable components, and gel polymer electrolytes for heightened mechanical strength and flexibility. Additionally, the review explores the integration of nanomaterials and additives to optimize electrolyte performance, addressing challenges related to ion transport and electrode-electrolyte interfaces. Moreover, the review scrutinizes the implications of emerging electrolyte materials on LIB sustainability, considering factors such as resource availability, recyclability, and environmental impact. The potential widespread adoption of these materials in commercial applications is examined, emphasizing the significance of scalability, cost-effectiveness, and regulatory considerations. By addressing crucial performance and safety aspects, these advancements pave the way for sustainable energy storage solutions crucial for the transition towards a cleaner and more energy-efficient future.
Keywords: Lithium-ion batteries, Charging, Lithium compounds, Electrolytes, Sustainable storage, Material , Energy storage.
... The challenge arises from the coordination of disparate sensor technologies, given the variations in communication protocols and data formats [43,48]. Achieving seamless integration requires a meticulous approach, considering the unique requirements of each discipline [44,[119][120][121][122][123] and developing adaptable systems capable of accommodating a wide range of sensor types. ...
The Architecture, Engineering, and Construction (AEC) industry is experiencing a profound transformation due to the rapid evolution of sensor technologies. These advancements present unprecedented opportunities for heightened monitoring and control throughout the entire project lifecycle. This research paper provides a thorough examination of the integration of cutting-edge sensors in AEC, delving into a variety of sensor types and their applications that propel the industry forward. Commencing with an exploration of pivotal sensor technologies, the review encompasses key components of modern AEC practices. Notably, LiDAR (Light Detection and Ranging), RFID (Radio-Frequency Identification), IoT (Internet of Things) devices, and advanced imaging sensors are scrutinized. Each sensor type undergoes evaluation based on precision, data acquisition speed, and its suitability for specific AEC tasks. The analysis begins by dissecting the application of LiDAR sensors, focusing on their role in precise 3D mapping. This capability facilitates high-resolution scans of construction sites, thereby enhancing project planning and management. RFID technology is then examined in the context of real-time asset tracking and material management, contributing to streamlined logistics and shortened project timelines. Additionally, the integration of IoT devices is investigated for their ability to establish a network of interconnected sensors, fostering real-time communication and data sharing among various construction components. Moreover, the review underscores the transformative impact of advanced imaging sensors, such as thermal cameras and hyperspectral imaging. These sensors play a crucial role in evaluating structural integrity and monitoring environmental conditions. By enabling the early detection of potential issues, AEC professionals can ensure enhanced safety and longevity of structures. The synergistic integration of diverse sensor technologies provides a comprehensive approach to monitoring and controlling various aspects of construction projects. This, in turn, opens avenues for increased efficiency, safety, and sustainability within the AEC industry.
Keywords: Sensor, Internet of Things, Artificial Intelligence, Virtual reality, Augmented reality, Construction industry, Architecture
... For instance, sentiment analysis evaluates public opinions on social media platforms, offering real-time insights into market sentiment. Such information is crucial for anticipating movements and adjusting strategies accordingly [44][45][46][47][48]. ...
This research paper investigates the profound influence of Artificial Intelligence (AI) on financial forecasting and its pivotal role in molding the trajectory of investment strategies. In the face of the dynamic nature of financial markets, traditional approaches encounter challenges, prompting the emergence of state-of-the-art AI technologies as indispensable instruments for forecasting trends, managing risks, and refining investment choices. The study delves into a spectrum of cutting-edge AI technologies, models, tools, and frameworks reshaping the realm of financial forecasting. Examining Machine Learning (ML) algorithms, such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), reveals their capacity to discern intricate patterns in financial data, thereby elevating predictive accuracy. Deep Learning techniques, including convolutional neural networks (CNNs), are scrutinized for their adeptness in extracting hierarchical features from diverse datasets, contributing to more resilient forecasts. Additionally, the research underscores the importance of natural language processing (NLP) and sentiment analysis in appraising market sentiments and integrating qualitative information into forecasting models. The paper explores advanced AI-driven tools like algorithmic trading systems and robo-advisors, elucidating their roles in automating investment strategies and optimizing portfolio management through real-time market data. The incorporation of Reinforcement Learning (RL) in financial forecasting is examined, showcasing how adaptive, learning-based approaches enhance decision-making in dynamic market environments. The paper also addresses nascent technologies such as quantum computing and their potential influence on financial modeling, heralding a new era of computational power for intricate simulations and scenario analysis. The paper offers a comprehensive overview of the evolving landscape, emphasizing the imperative for continuous innovation and adaptation to thrive in the swiftly changing realm of finance.
Keywords: Artificial intelligence, financial forecasting, Finance, Investment, Financial markets, Commerce, Stock market.
... Drones equipped with cameras provide a comprehensive aerial perspective, facilitating real-time analysis and decision-making. AI like algorithms can identify potential safety hazards, monitor worker activities, and assess project progress by scrutinizing images and videos [6, [58][59][60][61][62][63]. ...
The fusion of Artificial Intelligence (AI) and the Internet of Things (IoT) has brought about a paradigm shift in the realm of architecture, engineering, and construction (AEC), introducing intelligent sensing technologies that significantly enhance monitoring and control. This study delves into the varied applications, hurdles, and prospects emerging from the collaborative deployment of AI and IoT-based sensors within the AEC domain. AI-equipped smart sensors enable real-time monitoring of structural health, energy consumption, and environmental conditions in both buildings and infrastructure. These technologies empower predictive maintenance, ensuring the durability of structures while minimizing downtime. Additionally, AI-driven analytics optimize resource allocation, improve safety protocols, and streamline construction processes, thereby enhancing overall project efficiency. Through ongoing analysis of data collected by sensors integrated into HVAC systems, elevators, and lighting, maintenance teams can pre-emptively tackle potential malfunctions. Furthermore, the synergy between AI and IoT enables the development of intelligent buildings with adaptive features. Sensors that examine occupancy patterns, lighting preferences, and temperature fluctuations play a pivotal role in crafting energy-efficient and occupant-centric building designs. The security and privacy concerns associated with sensor-generated data give rise to critical issues that necessitate robust cybersecurity measures. Interoperability challenges among diverse sensor networks and AI platforms also present obstacles to seamless integration. Furthermore, the adoption of these technologies demands substantial investments in infrastructure and workforce training, requiring a strategic approach for widespread acceptance. The paper explores how the predictive capabilities of AI-driven sensors contribute to risk mitigation and cost reduction across the entire project lifecycle. Moreover, the ability to collect and analyze vast amounts of data empowers stakeholders to make well-informed decisions, fostering innovation and sustainability in the AEC industry. By addressing pivotal issues and underscoring potential benefits, it provides invaluable insights for industry professionals, researchers, and policymakers eager to harness the transformative potential of intelligent technologies in architecture, engineering, and construction.
Keywords: Artificial Intelligence, Internet Of Things, Sensors, Monitoring, Construction industry, Smart city, Automation, Construction sites.
... Partnerships for the Goals (SDG 17): Blockchain's decentralized nature facilitates transparent and accountable partnerships [26,27]. Smart contracts automate and enforce agreements, promoting trust among stakeholders in global collaborations for sustainable development [29, [124][125][126][127][128]. ...
The Metaverse, an immersive virtual reality realm where users engage with computer-generated environments, is swiftly emerging as a transformative platform poised to address and propel Sustainable Development Goals (SDGs). This research delves into the intersections between the Metaverse and SDGs, shedding light on how this cutting-edge technology can be harnessed to propel sustainable development across diverse sectors. Commencing with a comprehensive overview, the paper details the evolution, key components, and immersive capabilities of the Metaverse. Underscoring its dynamic and interactive essence, the research emphasizes the potential of the Metaverse to revolutionize conventional approaches to education, healthcare, environmental conservation, and economic empowerment. A thorough analysis unveils the Metaverse's role in democratizing education by transcending geographical boundaries and providing inclusive learning environments. The immersive nature of the Metaverse facilitates experiential learning, enhancing education accessibility, and fostering global collaboration to fulfill SDG 4 (Quality Education). Additionally, the paper explores how the Metaverse can contribute to healthcare solutions through virtual medical consultations, training healthcare professionals, and simulating medical scenarios. This innovative approach holds the potential to address healthcare disparities, advance SDG 3 (Good Health and Well-being), and improve overall public health. Environmental sustainability takes center stage as the Metaverse serves as a platform for raising awareness about climate change, promoting sustainable practices, and simulating eco-friendly solutions. This aligns with SDG 13 (Climate Action) and SDG 15 (Life on Land), emphasizing the Metaverse's capacity to inspire real-world environmental stewardship. Economic empowerment is addressed through discussions on virtual economies within the Metaverse, highlighting opportunities for entrepreneurship, job creation, and financial inclusion. This exploration corresponds to SDG 1 (No Poverty), SDG 8 (Decent Work and Economic Growth), and SDG 10 (Reduced Inequality).
Keywords: Metaverse, Sustainable Development Goals, SDGs, Sustainable development, Virtual reality, Sustainability, Blockchain, Augmented reality.
... This empowers financial institutions to make data-driven decisions in areas such as risk management, investment strategies, and customer service. Such synergy between blockchain and AI transforms data into a strategic asset, fostering innovation and improving overall business outcomes [37,39,[133][134][135][136][137]. ...
The convergence of Blockchain technology and Artificial Intelligence (AI) is exerting a transformative influence, ushering in a new epoch of security and transparency within the financial sector. This amalgamation effectively addresses pivotal challenges faced by conventional financial systems, presenting inventive solutions to heighten efficiency, diminish fraud, and amplify transparency. Blockchain, functioning as a decentralized and tamper-resistant ledger, introduces a paradigm shift in financial transactions. Its capacity to establish an unalterable record of transactions ensures that once data is recorded, it remains impervious to modification, thereby furnishing an unparalleled level of security. This inherent security attribute positions Blockchain as an optimal choice for reinforcing financial systems against cyber threats and fraudulent activities. On the other hand, AI contributes predictive analytics, machine learning, and automation to the forefront of financial operations. The integration of AI in finance enables real-time data analysis, risk assessment, and decision-making, optimizing processes and elevating overall efficiency. When amalgamated with Blockchain, AI augments the precision and dependability of financial data, cultivating a more secure and transparent ecosystem. A pivotal aspect of this integration in finance is the streamlining of Know Your Customer (KYC) and Anti-Money Laundering (AML) processes. The decentralized nature of Blockchain facilitates secure storage of customer data, mitigating the risk of identity theft, while AI algorithms adeptly analyze extensive datasets to pinpoint and flag suspicious activities. This not only augments security but also ensures adherence to regulatory requirements. Smart contracts, a distinctive feature of Blockchain, automate and enforce contractual agreements, diminishing the reliance on intermediaries and minimizing the probability of human error. AI algorithms can be seamlessly integrated into these contracts to enhance their adaptability and responsiveness to evolving market conditions, further refining financial processes. The transparency ushered in by Blockchain ensures that all stakeholders have access to a singular version of the truth, fostering trust in financial transactions. Furthermore, the incorporation of AI in fraud detection and risk management heightens the proactive identification of potential threats, safeguarding financial institutions and their clientele. As financial institutions increasingly embrace this integration, the industry stands on the brink of a revolution that not only safeguards against existing challenges but also paves the way for innovative and efficient financial ecosystems.
Keywords: Blockchain, Artificial Intelligence, Finance, Green finance, Commerce, Economic development, Forecasting.
... Cutting-edge AI technologies are transforming climate control systems by introducing adaptability and intelligence [110][111][112][113][114]. Machine learning algorithms, a subset of AI, scrutinize historical and real-time data to anticipate patterns and optimize heating, cooling, and ventilation systems accordingly. Such systems assimilate insights from user behaviours, external weather conditions, and occupancy patterns to make real-time adjustments, ensuring a harmonious balance between energy efficiency and thermal comfort [111,113,[115][116][117][118][119]. ...
This research paper offers a thorough examination of strategies geared towards enhancing thermal comfort within constructed spaces through the incorporation of state-of-the-art design, monitoring, and optimization technologies. In light of the escalating challenges brought about by climate change and the increasing demand for sustainable and energy-efficient building solutions, the quest for improved thermal comfort has emerged as a central focus in architectural and engineering research. The paper initiates its exploration by delving into contemporary design principles that underscore the significance of passive strategies, such as optimal building orientation, shading devices, and natural ventilation. It elucidates the synergies between architectural elements and thermal comfort, emphasizing how innovative designs can foster ideal indoor conditions while reducing reliance on energy-intensive heating, ventilation, and air conditioning (HVAC) systems. A substantial segment of the review is dedicated to monitoring technologies enabling real-time assessment of indoor environmental parameters. The integration of sensors, data analytics, and Building Information Modelling (BIM) facilitates a nuanced comprehension of thermal comfort dynamics, allowing for adaptive responses to evolving conditions. The paper discusses the role of wearable devices and occupant feedback systems in capturing subjective thermal perceptions, thereby enriching the data pool for a more comprehensive analysis. Moreover, the review delves into the burgeoning field of optimization technologies, encompassing the utilization of machine learning algorithms and artificial intelligence in building management systems. These technologies empower predictive modeling of thermal comfort, optimizing HVAC operations, and minimizing energy consumption. The synthesis of these technologies not only enhances occupant well-being but also aligns with global endeavours to forge more sustainable and resilient built environments amid evolving climate challenges.
Keywords: Thermal comfort, Energy utilization, Energy efficiency, Air conditioning, Ventilation, Cooling, Energy conservation, Atmospheric temperature.
... A primary application of AI and ML in wastewater treatment lies in real-time monitoring and control [44][45][46][47][48]. Unlike traditional methods relying on periodic sampling and laboratory analysis [10, [49][50][51][52][53], these technologies enable continuous real-time monitoring of key parameters such as pH, dissolved oxygen, and nutrient levels. Advanced sensors and data analytics algorithms facilitate automatic adjustments to process parameters, ensuring optimal conditions for biological and chemical treatment processes. ...
The escalating global demand for clean water mandates the development of inventive approaches to wastewater treatment systems. This paper investigates the incorporation of cutting-edge technologies, specifically Artificial Intelligence (AI), Machine Learning (ML), Blockchain, and the Internet of Things (IoT), to revolutionize and elevate wastewater treatment procedures. The collaborative application of these technologies presents a promising avenue for optimizing efficiency, sustainability, and overall performance within water treatment infrastructure. Artificial Intelligence and Machine Learning play pivotal roles in the predictive modeling and decision-making processes within wastewater treatment plants. These technologies facilitate real-time monitoring of water quality parameters, enabling dynamic adjustments to treatment protocols based on data-driven insights. The adaptive nature of AI and ML algorithms enhances system resilience, diminishes operational costs, and ensures adherence to rigorous environmental standards. The integration of Blockchain technology introduces a decentralized and secure framework for managing data in wastewater treatment systems. By capitalizing on the inherent transparency and immutability of blockchain, stakeholders can trace the complete lifecycle of water treatment, from source to discharge. This not only promotes accountability but also nurtures trust among regulators, utilities, and the public, fostering a more transparent and sustainable water management ecosystem. Moreover, the Internet of Things contributes to the establishment of a connected and responsive wastewater treatment infrastructure. Sensor networks embedded throughout the treatment process enable real-time data collection, facilitating remote monitoring and control. The seamless communication between IoT devices ensures prompt identification of anomalies and potential system failures, allowing for timely intervention and averting environmental hazards. This research not only underscores the potential of these technologies but also emphasizes the significance of their integration to address the evolving challenges in water management and contribute to a more sustainable future.
Keywords: Wastewater treatment, Waste water management, Artificial Intelligence, Machine Learning, Blockchain, Internet of Things, Adsorption, Effluents
... AI-driven tools can provide personalized support to online learners, making distance education more e ective and accessible. [18,[63][64][65][66][67] Collaboration Between Humans and AI e future of AI in education involves closer collaboration between humans and intelligent machines. is partnership can lead to more e ective teaching and learning experiences, with AI handling routine tasks and providing valuable insights. ...
This research paper explores how the integration of Artificial Intelligence (AI) in the education sector is bringing about transformative changes, particularly within the frameworks of Education 4.0 and 5.0. In response to the evolving technological landscape, education is undergoing a shift to address the challenges of the 21st century, moving away from traditional models to embrace more personalized and adaptive approaches. Education 4.0 represents a significant shift where technology, notably AI, is harnessed to enhance the learning experience. The paper investigates the utilization of AI technologies, such as machine learning algorithms and natural language processing, to create personalized learning environments. These environments are designed to meet the specific needs and preferences of individual learners, fostering a more engaging and effective educational experience. The move from Education 3.0 to 4.0 signifies a departure from standardized, one-size-fits-all approaches to education, embracing a more dynamic and responsive system. Expanding on the principles of Education 4.0, Education 5.0 takes the integration of AI in education a step further by emphasizing adaptive learning. The paper delves into the concept of adaptive learning, exploring how AI systems can dynamically adjust instructional strategies based on real-time feedback and learner progress. Education 5.0 seeks to optimize the learning journey by tailoring content, pace, and assessments to each student's abilities and learning style, aiming to improve overall educational outcomes. Additionally, the research examines the challenges and ethical considerations associated with the widespread adoption of AI in education. It critically evaluates issues related to data privacy, bias in AI algorithms, and the potential impact on teacher-student relationships. The findings emphasize the need to strike a balance between technological innovation and ethical considerations, ensuring the responsible and effective integration of AI in personalized and adaptive learning environments.
Keywords: Artificial Intelligence, Education 4.0, Education 5.0, learning, Industry 4.0, Medical education, Engineering education.
... Metaverse models serve as the foundational frameworks that define the structure, rules, and interactions within the virtual space [54,55]. Such models play a significant role in shaping service quality [54,[56][57][58][59][60][61]. ...
In the dynamic realm of digital technology, the Metaverse has emerged as a revolutionary platform with vast potential for businesses aiming to strengthen customer loyalty. This study investigates effective techniques within the Metaverse to enhance customer relationships, service, engagement, satisfaction, and overall experience. As businesses increasingly migrate to virtual spaces, understanding the nuances of customer interaction within the Metaverse becomes crucial. The research employs a comprehensive approach, blending qualitative and quantitative methods to explore the diverse aspects of customer loyalty in the Metaverse. Qualitative methods, such as in-depth interviews and participant observations in virtual environments, offer profound insights into customer behavior and expectations. Quantitative surveys provide structured data, enabling statistical analysis of customer satisfaction, engagement, and loyalty metrics. The findings underscore the significant impact of effective Metaverse utilization on customer loyalty. Tailored avatars, immersive virtual environments, and interactive experiences foster a sense of belonging and personalization, nurturing a deeper emotional bond between customers and brands. Real-time customer support and personalized services within the Metaverse elevate customer satisfaction and trust, leading to heightened levels of loyalty. Additionally, the research outlines key strategies for businesses to implement in the Metaverse, including gamification, social interactions, virtual events, and augmented reality integrations. Gamified experiences enhance customer engagement and retention, while social interactions cultivate a sense of community, encouraging customers to advocate for the brand. Virtual events and augmented reality applications offer innovative platforms for businesses to showcase products, educate customers, and facilitate meaningful interactions, thereby bolstering customer loyalty. This study provides valuable insights into the transformative potential of the Metaverse in shaping the future of customer-business relationships. By embracing the identified strategies, businesses can not only adapt to the evolving digital landscape but also cultivate enduring customer loyalty, ensuring sustainable growth and success in the dynamic Metaverse market.
Keywords: Metaverse, Customer satisfaction, Sustainability, Marketing, Service Quality, Customer relationship management, Customer loyalty.
This paper undertakes a comprehensive examination of the framework, applications,
challenges, and future development prospects of metaverse technologies within the educational
domain, focusing on a diverse array of techniques that contribute to the continuous evolution of this
innovative educational landscape. The framework analysis delves into the design and
implementation strategies that form the foundation of metaverse-based educational platforms.
Utilizing augmented reality (AR), virtual reality (VR), and mixed reality (MR), these frameworks
strive to create dynamic and captivating learning environments that transcend geographical
limitations. Studies and examples illustrate their utilization across disciplines, spanning from science
and mathematics to humanities and vocational training. The paper underscores the significance of
artificial intelligence (AI) and machine learning (ML) algorithms in tailoring educational content to
individual learning styles, thereby enhancing personalization and adaptability within metaverse-
based educational systems. Looking ahead, the paper outlines potential developments in metaverse
education, exploring emerging technologies and trends such as blockchain -based credentialing,
haptic feedback integration, and the incorporation of the Internet of Things (IoT). The paper
acknowledges the diverse techniques contributing to its ongoing evolution, emphasizing the dynamic
nature of this transformative educational landscape.
Keywords: Metaverse, Education, Learning, Virtual reality, Augmented reality, E-learning,
Students, Artificial intelligence.
The rapid progression of technology has paved the path for the convergence of Artificial Intelligence (AI), Internet of Things (IoT), and big data within the Architecture, Engineering, and Construction (AEC) industry. This convergence has given rise to intelligent and sustainable construction paradigms,exemplified by Construction 4.0 and Construction 5.0. This research delves into the challenges and future directions associated with seamlessly integrating these cutting-edge technologies in the AEC sector. The discussion begins withan in-depth exploration of the foundational aspects of intelligent and sustainable construction. It focuses on the crucial roles played by IoT technology, advanced computing models, and big data technology. The research then delves into the various AI models and techniques that are reshaping the landscape of Construction 4.0 and 5.0, along with the broader societal changes embodied in Society 5.0. The research underscores the significance of these AI-driven solutions in optimizing resource management, streamlining operational processes, and enhancing decision-making within the AEC field. Furthermore,the study highlights the synergistic relationship between Blockchain technology and its applications in conjunction with AI and IoT. This convergence of technologies not only enhances transparency and security in transactions but also facilitates the implementation of efficient and sustainable construction practices in alignment with societal and environmental needs. Throughout the exploration of these technological advancements, the research underscores the integration of the Sustainable Development Goals (SDGs) as a vital guiding framework for shaping future initiatives within the AEC sector. This research services as a roadmap for industry stakeholders, policymakers, and researchers,emphasizing the importance of strategic collaboration and innovative solutions to address challenges and unlock the full potential of AI, IoT, and big data technologies for intelligent and sustainable AEC practices. Keywords: Artificial intelligence, Construction 4.0, Internet of things, Construction5.0, Big data, Industry 4.0, Smart construction, Sustainable construction.
In the dynamic world of technology, ChatGPT has emerged as a transformative tool within the realm of Arts and Humanities, facilitating innovative interactions between humans and machines. This research delves into the multifaceted role of ChatGPT in the Arts and Humanities, illuminating its potential applications and the challenges it presents. ChatGPT serves as a creative collaborator, providing artists and humanists with a distinctive platform for ideation, brainstorming, and refining their artistic expressions. Through natural language conversations, it stimulates creative thinking processes, offering valuable insights and diverse perspectives. In the realm of Humanities, ChatGPT acts as a crucial research assistant, supporting scholars in tasks like data analysis, literature review, and information synthesis. Its ability to process vast textual data expedites research, allowing scholars to delve deeper into their inquiries. However, integrating ChatGPT into the Arts and Humanities domain is not without its challenges. Ethical considerations, such as data privacy and bias, require careful attention. Balancing the augmentation of human creativity with the preservation of genuine artistic expression is a complex task. Additionally, ensuring accessibility and inclusivity in ChatGPT-generated content remains a concern, underscoring the need for continuous refinement and awareness. This research explores the evolving landscape of Arts and Humanities, where ChatGPT acts as a catalyst for creativity, critical thinking, and scholarly endeavors. By addressing these challenges and harnessing its potential, ChatGPT stands poised to bridge the gap between technology and the intricate nuances of human creativity in unprecedented ways.
This research paper delves into the potential for transformation by incorporating cutting-edge technologies such as Artificial Intelligence (AI), Blockchain, Internet of Things (IoT), Augmented Reality (AR), and Virtual Reality (VR) to foster the sustainable development of tourism. The contemporary tourism industry confronts unprecedented challenges, including environmental degradation, cultural disruption, and socio-economic imbalances. This investigation explores the innovative application of advanced technologies as a comprehensive solution to tackle these challenges and propel the tourism sector toward a more sustainable future. Artificial Intelligence emerges as a pivotal facilitator in enhancing tourism operations and decision-making processes. Leveraging predictive analytics, machine learning, and natural language processing, AI contributes to optimized resource allocation, personalized visitor experiences, and the formulation of data-driven sustainability strategies. Blockchain technology, recognized for its decentralized and transparent nature, is utilized to establish secure and traceable tourism supply chains, ensuring equitable economic distribution and minimizing the environmental impact of travel. The Internet of Things (IoT) assumes a central role in creating intelligent and interconnected tourism ecosystems. IoT devices enable real-time monitoring of environmental conditions, empowering destination management organizations to implement adaptive measures for conservation. Furthermore, IoT devices enhance visitor safety and satisfaction through context-aware services. Augmented Reality (AR) and Virtual Reality (VR) technologies offer immersive experiences that redefine how tourists interact with destinations. AR enriches the cultural and historical context of attractions, fostering a deeper understanding and appreciation of local heritage. Meanwhile, VR enables virtual tourism experiences, reducing the need for physical travel and consequently mitigating carbon footprints. The findings underscore the potential of a technology-driven approach to redefine tourism practices, ensuring long-term environmental, social, and economic sustainability.
Keywords: Sustainable tourism, Sustainable development, Artificial Intelligence, Blockchain, Internet of Things, Augmented reality, Virtual reality.
The construction sector is characterized by dynamic and perilous conditions, demanding innovative strategies to bolster worker safety. This research delves into the incorporation of cutting-edge wearable technologies as a proactive measure to tailor safety protocols for construction sites. Focused on risk mitigation and overall safety enhancement, this investigation assesses the efficacy of various advanced wearable devices. The study commences by scrutinizing the role of intelligent helmets equipped with augmented reality (AR) technology. These helmets furnish real-time data about the construction site, delivering critical information to workers, including potential hazards, emergency exits, and equipment statuses. The integration of AR heightens situational awareness, empowering workers to make well-informed decisions and respond promptly to changing conditions. Beyond smart helmets, the research explores the implementation of wearable sensors designed to monitor the physiological parameters of construction workers. Capable of measuring vital signs such as heart rate, body temperature, and fatigue levels, these sensors enable continuous assessment of worker health. The data generated can be analyzed to identify patterns and trends, facilitating the implementation of preventive measures to address health-related risks. Additionally, the study delves into the adoption of exoskeletons as wearable devices to enhance human capabilities and diminish the risk of musculoskeletal injuries. These exoskeletons provide ergonomic support, particularly during tasks involving heavy lifting or repetitive motions. By assisting workers in maintaining proper posture and reducing physical strain, exoskeletons contribute to long-term injury prevention and improved overall well-being. Integration with artificial intelligence (AI) algorithms enhances the capabilities of these sensors, providing a nuanced understanding of individual health patterns and personalized safety recommendations. The integration of smart helmets, wearable sensors, and exoskeletons presents a multifaceted approach to personalized safety, addressing both environmental and physiological factors. As the construction industry continues to evolve, the adoption of these technologies holds the potential to not only enhance safety outcomes but also redefine the future of work in hazardous environments.
Keywords: Wearable technology, Wearable devices, Construction industry, Accident prevention, Occupational risk, Wearable sensor, Construction safety.
The convergence of 4D, 5D, and 6D printing technologies with the Architecture, Engineering, and Construction (AEC) sector has ushered in an era of unparalleled innovation and efficiency. This study delves into the diverse applications, obstacles, and forthcoming advancements of these cutting-edge printing technologies within the AEC industry. 4D printing, marked by materials that can self-transform or assemble over time, has revolutionized architectural design by enabling dynamic structures that respond to environmental stimuli. 5D printing seamlessly integrates cost estimation into the fabrication process, ensuring real-time project budgeting and management. Additionally, 6D printing incorporates environmental and sustainability data, providing architects and engineers with insights into the long-term environmental impact of their designs. These applications collectively enhance the AEC industry's capacity to create structures that are sustainable, cost-effective, and adaptable. Despite the promising applications, challenges persist. Material constraints, scalability of production, and the need for standardized processes present significant obstacles. Furthermore, integrating intricate data streams for 5D and 6D printing demands robust computational algorithms and efficient data management systems. Ethical concerns surrounding intellectual property, data security, and environmental impact also require careful consideration. To surmount these challenges, ongoing research focuses on advancing material science, innovative fabrication techniques, and the development of intelligent algorithms. Collaboration among interdisciplinary teams, including architects, engineers, computer scientists, and environmental experts, is crucial to unlocking the full potential of these technologies. Additionally, the integration of artificial intelligence and machine learning algorithms holds the promise of streamlining the design process, optimizing material usage, and enhancing predictive modeling. This approach paves the way for a more sustainable and efficient AEC industry. Embracing these advancements is essential for the AEC sector to navigate the complexities of modern architecture, ensuring a sustainable and resilient built environment for future generations.
Keywords: 4D printing, 5D printing, 6D printing, Artificial Intelligence, Construction industry, Structural engineering, Building information modelling.
This study considers the spatial analysis and evaluation layout of electric vehicle charging infrastructures, taking the central urban area of Chongqing as an example. Mathematical model analysis, ArcGIS spatial analysis, field investigation, questionnaire measurement, and hierarchical analysis methods are utilized to discuss the current distribution characteristics and supply–demand matching of the electric vehicle charging infrastructure in this region. The resulting data can provide references for the optimal layout of charging infrastructure. The main conclusions of this study are as follows: (1) The configuration and demand of charging infrastructure in the central urban area of Chongqing have obvious spatial differentiation and show strong centrality. (2) It is a common phenomenon that the charging infrastructure in the central urban area of Chongqing is in short supply, and it is pressing that a new charging infrastructure be built. (3) In the process of construction and operation of charging infrastructure, various factors, such as economy and traffic, should be comprehensively considered; at the same time, incidents of inefficient operation, such as being crowded out by nonelectric vehicles and unmaintained facility failure, should be minimized.
Mobility and transportation activities in smart cities require an increasing amount of energy. With the frequent energy crises arising worldwide and the need for a more sustainable and environmental friendly economy, optimizing energy consumption in these growing activities becomes a must. This work reviews the latest works in this matter and discusses several challenges that emerge from the aforementioned social and industrial demands. The paper analyzes how collaborative concepts and the increasing use of electric vehicles can contribute to reduce energy consumption practices, as well as intelligent x-heuristic algorithms that can be employed to achieve this fundamental goal. In addition, the paper analyzes computational results from previous works on mobility and transportation in smart cities applying x-heuristics algorithms. Finally, a novel computational experiment, involving a ridesharing example, is carried out to illustrate the benefits that can be obtained by employing these algorithms.
Vehicular emissions have become important sources of air pollution in China. Regarding the environmental impacts of vehicle emission control policies (VECPs), changes in air pollutants and CO2 emissions have attracted more attention. Hainan is the first province in China declared to ban the sale of fuel-powered cars by 2030, aiming to accelerate cutting down the local air pollution emissions. However, there is no previous study examining how these VECPs would affect air pollutants in Hainan. Further, research on whether the controls would lead to a real carbon reduction is limited. Therefore, this paper quantitatively assesses the emission changes of primary air pollutants (including NOx, CO, VOCs, PM2.5, PM10, and PMTSP) and greenhouse gases (CO2, CH4, and N2O) in the transportation sector with regard to different VECPs in Hainan. The results reveal that (1) VECPs would lead to significant increases in vehicular population by 21 %-65 % in 2025-2050. Specifically, light-duty cars and buses with 4-stroke engines (LD4Cs) is the largest contributor and banning sales of fuel-powered vehicles would lead to a larger increase of 1914.6 thousand (64 %) in 2030; (2) for air pollutant emissions, the policy scenario would bring notable reduction effects, decreasing by 1.0 %-16.0 % and 16.7 %-38.7 % in 2030 and 2050 (PM excluding), respectively, suggesting VECPs play important roles in alleviating environmental pollution; (3) conversely, for CO2 emissions, the policy scenario would cause increases of 0.8 Mt. (17.8 %) and 0.3 Mt. (6.1 %) in 2035 and 2050, respectively, indicating promoting new energy vehicles (NEVs) would increase carbon emissions. Meanwhile, it suggests that CO2 emission in the transportation sector of Hainan peaked in 2020. This research highlights that VECPs would be a double-edged sword, leading to air pollutants reductions but not necessarily decline CO2 emissions. This fact would further accelerate mechanism and technological innovation in transport to alleviate air pollution and carbon emissions simultaneously.
For the first time, we quantify cost, footprint, and reliability implications of deploying hydrogen-based generation in off-grid electric vehicle charging stations (CS) using an optimization model coupled with a geographic information system (GIS) analysis for the city of Riyadh, Saudi Arabia. We also account for the challenges associated with wind energy deployment as a candidate generation technology within city centers. The analysis was restricted to carbon-free technologies: photovoltaics (PV), wind, battery, and hydrogen fuel-cells. At current prevailing technology costs, hydrogen can reduce the required footprint of off-grid CSs by 25% at a small incremental cost increase without impacting the charging reliability. By 2030, however, hydrogen will simultaneously provide the footprint and cost advantages. If we allow as little as 5% of the annual load to be unmet, the required footprint of the CS decreases by 60%. The levelized cost of energy values for the CS by 2030 can range between 0.13 and 0.20 $/kWh depending on learning-curve assumptions. The footprints calculated are then mapped to five land parcel categories in Riyadh: gas station, hospital, mall, school, and university. Incorporating hydrogen in CS design increases the number of parcels that could accommodate CSs by 15–45% via reducing the required PV array (i.e., footprint).
Electric vehicles (EVs) will be the next technological leap for urban mobility, however the market penetration rate depends on several factors, including the major hurdle of limited EV charging stations. Equity is also an important factor – ensuring EV charging stations are widespread and available for all segments of the population. This paper outlines an innovative methodology to systematically determine the locations of EV charging stations while considering equity and efficiency to maximize accessibility and usage. The methodology has two levels. First, solving what is known as the Set Covering Location Problem (SCLP) by determining a threshold so that the distance (or travel time) between the consumer and the EV charging station will be less than or equal to a given value. This is a policy-based decision and provides a framework to ensure EV charging stations are ubiquitous and equitable. Second is solving the Maximum Covering Location Problem (MCLP) by considering a series of evaluation criteria to satisfy the demand of the early adopters. Following evaluation of the SCLP and MCLP, selected locations are aggregated by partitions to develop larger scale hubs. These hubs will not only include EV charging stations but will also function as connection points that integrate different modes of transportation.
The present study attempts to select and delineate various groundwater potential zones for the assessment of groundwater availability in a coastal part of Orissa using remote sensing and GIS technique. Satellite IRS-IC LISS III, Landsat TM digital and SRTM data have been used in the present study to prepare various thematic maps, viz., landuse, geomorphological, geological, slope, drainage density, lineament density map. On the basis of relative contribution of each of these maps towards groundwater potential, the weight of each thematic map has been selected. Further, within each thematic map ranking has been made for each of the features. All the thematic maps are then registered with one another through ground control points and integrated step by step using the normalized aggregation method in GIS for computing groundwater potential index. On the basis of this final weight and ranking, the ground water potential zones have been delineated. Thus from the present study it is observed that an integrated approach involving remote sensing and GIS technique has been successfully used in identifying potential groundwater zones in the study area. Four categories of groundwater potential zones, viz., Very Good, Good, Poor and Very Poor have been demarcated. Major portions of the study area have “Very Good” as well as “Good” prospect while a few scattered areas have very poor prospect.
We live in a complex world characterised by complex people, complex times, and complex social, technological, economic, and ecological environments. The broad aim of our work is to investigate the use of ICT technologies for solving pressing problems in smart cities and societies. Specifically, in this paper, we introduce the concept of deep journalism, a data-driven deep learning-based approach, to discover and analyse cross-sectional multi-perspective information to enable better decision making and develop better instruments for academic, corporate, national, and international governance. We build three datasets (a newspaper, a technology magazine, and a Web of Science dataset) and discover the academic, industrial, public, governance, and political parameters for the transportation sector as a case study to introduce deep journalism and our tool, DeepJournal (Version 1.0), that implements our proposed approach. We elaborate on 89 transportation parameters and hundreds of dimensions, reviewing 400 technical, academic, and news articles. The findings related to the multi-perspective view of transportation reported in this paper show that there are many important problems that industry and academia seem to ignore. In contrast, academia produces much broader and deeper knowledge on subjects such as pollution that are not sufficiently explored in industry. Our deep journalism approach could find the gaps in information and highlight them to the public and other stakeholders.
Agriculture is the primary source of income in the Sub-Himalayan Jalpaiguri District; therefore, identifying the optimal use of existing agricultural land is crucial. The primary objective of this study is to identify potential agricultural sites in the Jalpaiguri District for the sustainable development of the region. About eleven parameters have been considered using the multi-influencing technique in combination with remote sensing (RS) and geographic information system to delineate and model potential agriculture sites. The final agriculture suitability map was created using the ‘Weighted Overlay technique,’ and the final output can be categorized into five classes, i.e., highly suitability (424.3 km²), moderately suitability (1191.8 km²), marginally suitable (1141.4 km²), currently not suitable (567.1 km²), and permanently not suitable (60.8 km²). Besides, the results have been thoroughly verified using Google Earth images, in comparison with Landsat 8 output, and field visits using GPS to increase the reliability of the results. The finding reveals potential outcomes for agricultural activity; however, building a sustainable management strategy and resilient farming practices should be adopted to boost the region’s agricultural output. Farmers, regional planners, and government officials can use the current agriculture suitability map to make comprehensive judgments for the region, such as determining the potential for agriculture sites, improving agricultural growth, and promoting self-reliant local economies.
Graphical abstract
Electric vehicles (EVs) have the advantages of low pollution, low energy consumption, and high energy efficiency, so they are highly valued by governments, enterprises, and consumers. However, the promotion and use of electric vehicles is restricted to a certain extent because of their limited range. This paper selects electric vehicle intercity medium- and long-distance travel as the research object, and takes the classical flow-capturing location problem as the theoretical basis for the expressway network or national highway network. This paper also considers the driver’s charging choice behavior and range anxiety, studies the electric vehicle charging station location problem, establishes the charging station location model, and uses the Tabu search algorithm to solve the problem. Finally, the effectiveness of the model and algorithm is verified by empirical analysis. The results show that the charging station location model considering the driver’s charging choice behavior and range anxiety performs better.
Achieving net-zero carbon in the UK by 2050 will necessitate the decarbonisation of the transportation systems. However, there are challenges to this, especially for vehicles in cities where the charging infrastructure is at its minimum. Overcoming these challenges will undoubtedly encourage electrical vehicle (EV) use, with commensurate reductions in emission coupled with better environmental conditions in cities, e.g., air quality. Drivers, on the whole, are reluctant to invest in an EV if they cannot access a convenient charging point within their living area. This research provides a methodology to support the planning for the optimum siting of charging infrastructure, so it is accessible to as many citizens as possible within a city. The work focuses on Winchester City and District in the UK. The multi-criteria decision approach is based on the Analytical Hierarchy Process (AHP) linked to site spatial assessment using Geographical Information System (GIS). The assessment considered key criteria such as road type, road access, on-road parking availability, road slope, proximity to fuel stations, current/planned charging points, car parks and population distributions. The process contains two suitability filters, namely, restricted road and suitability mask. In the first, all restricted roads were excluded from further analysis, which resulted in reducing the road segments from over 9000 to around 2000. When applying the second filter an overall result of 44 suitable EV charging point locations was achieved. These locations were validated using the Google Earth® imaging platform to check actual locations against those predicted by the analysis. The presented methodology is accurate and is generalisable to other cities or regions.
Global warming, change in climate and rapid growth of population have put a huge strain on the world's groundwater resources. The use of excess groundwater has created a drop in the water table. Increased use of groundwater for agriculture in agro-based areas increases the demand for groundwater. The current study has been conducted in Cooch Behar, an agro-based remote district of North Bengal in the Indian state of West Bengal, where the use of high groundwater in agriculture has reduced groundwater levels. Comparative assessment of Multi-Influencing Factor (MIF) and Analytical Hierarchy Process (AHP) techniques has been used to create a perspective mapping of groundwater potential zone for the research region. Land use and land cover (LULC), rainfall, soil texture, geomorphology, lithology, drainage density, Normalized Differential Vegetation Index (NDVI), and Topographic Wetness Index (TWI) have been used to create the map. The MIF and AHP techniques have been used for Groundwater Potential Zones (GPZ) mapping. Output GPZ has been classified into 5 classes, i.e., very poor, poor, moderate, good, and excellent. MIF technique shows that the groundwater potential classes are covering 9% (285 km 2), 21% (67 km 2), 30% (944 km 2), 27% (837 km 2) and 13% (410 km 2) of the study area, respectively, whereas the output GPZ from AHP technique shows that the groundwater potential classes are covering 8% (271 km 2), 15% (508 km 2), 24% (813 km 2), 33% (1118 km 2) and 20% (677 km 2), respectively. Finally, the maps have been verified using groundwater fluctuation data through Receivers Operating Characteristic curve (ROC). The MIF technique's Area Under Curve (AUC) score is 76.5%, while the AHP technique reveals 88.9% accuracy. Both techniques for assessing and monitoring GPZ have been accurate and reasonable. Thus, this type of research is reliable for a more appropriate framework for swiftly analyzing groundwater recharge and directing the location of artificial recharge structures and other groundwater management operations of agricultural-based areas.
Geospatial techniques are very important tools for the assessment, management and conservation of groundwater as well as any natural resources. This study area i.e. Saltora block in Bankura district, West Bengal [WB], India falls under the Rarh region [semi-arid] of West Bengal which is prone to water scarcity. Keeping the concern about the following problems in mind, in this study, an integrated approach of the geospatial technique i.e. remote sensing and geographical information system [GIS] and multi influencing factors [MIF] has been applied to delineate the groundwater potential zones in this study. Six influential factors like geology, lineament, slope, LULC, drainage density, and soil texture have been used to delineate the groundwater potential zones, and their influences in groundwater potential were assessed by weightage scores using the MIF technique. All thematic layers were prepared using satellite data and imageries, conventional data sets, etc. in the GIS environment, all the layers were merged using the multi influencing factor (MIF) method and thus the groundwater potential zone [GWPZ] map was constructed as a result of overlay analysis. The total Saltora block was classified into five classes in terms of groundwater potential i.e., Very high potential zone [5.23%], high potential zone [36.02%], Moderate [41.8%], Low [15.05%], and last very low potential [1.90%]. Based on the overall potentiality scenario, the Gram Panchayats [GP] of the blocks have been ranked using by TOPSIS statistical technique and the result reveals that the Bamuntore and Salma GP ranked first; Tiluri, Dhekit, Saltora, and Kanuri GP were second followed by Pabra and Gogra GP respectively. The final GWPZ delineation result was validated through ground verification using 43 dug wells data. The cut-off independent measure of the ROC-AUC curve is employed in this study to validate the GMS outcome using model testing data. The accuracy value is 0.837 and the value of AUC indicates the acceptability of the model as the AUC value >0.75 which is considered a strong effect. In light of the aforementioned factors, the statistical model for the study is 0.754, which accurately projected groundwater potential. So, the generated map could be used as a preliminary reference for site selection of groundwater exploration.
The key features of the electric vehicle (EV) from urban area emphasizes on clean air as well as quiet running nature to preserve atmospheric pollution conditions in the city, however, the new public health risk is arising due to calm running environment of electric vehicles. Since the electric vehicles run at low noise levels, pedestrians being troubled in road traffic accidents which tend to focus on the safety of the pedestrian. The aim of the present study is to analyze the perception of pedestrian’s and driver’s with the quiet nature of electric vehicles by evaluating the data obtained through a questionnaire survey and interviews conducted at the Mumbai Metropolitan Region (MMR), India. A total of 398 drivers of various driving experiences and 401 pedestrians from various locations of MMR had been responded to acquire technical data. A hypothesis is evaluated for public health risk associated with the quiet nature of electric vehicles from the driver’s gender and age perspective. In addition, the ANOVA study was carried out to test the statistical significance of risk with respect to age, gender, vehicle usage, driving experience. The results illustrate that moderate risk is associated with the quiet nature of EV and more attention is required by road users as well as drivers, nevertheless gender (P=0.3321), profession (P=0.6537), driving experience (P=0.8888), vehicle use (P=0.3819) are not significant based on driver’s perception (P value greater than 0.05), whereas driver age group (P=0.0000) is accountable for perceived risk. Similarly, considering pedestrian’s perception, gender (P=0.7954), profession (P=0.8188), distance walk (P=0.2313), location (P=0.3896) are not significant. The outcome of this study recommends the foremost need for the advanced control system in electric vehicles.
There is a growing demand for lithium-ion batteries (LIBs) for electric transportation and to support the application of renewable energies by auxiliary energy storage systems. This surge in demand requires...
Urbanization in addition to employ of green technology has boosted the use of electric vehicles in the cities. Since electric vehicles are free from exhaust gases, there would be an improvement in environmental conditions; however, an unintentional upcoming risk is observed due to the silent feature of electric vehicles. Similarly, the rising trend of accidents causes major injury or even permanent disability which is strongly associated with human health and global safety. This study attempts to address three extensive objectives. Is human health risk associated with the silent nature of electric vehicles from the pedestrian's gender perspective? Is the safety of pedestrians depending upon the distance traveled by them? How the age group is responsible for any fatal or accidents? Quantitative primary data were collected from 401 pedestrian respondents through an extensive questionnaire survey carried out in Mumbai, India. The hypothesis is evaluated to justify the human health risk associated with the low noise of electric vehicles based on their technical response. In addition to ANOVA, a nonparametric tool was used to analyze perceived risk in the sense of human health. Utilizing the ground level essential information obtained from the road users, the authors have investigated the key risk associated with electric vehicles. The results showed that nominal/severe accidents may cause injury to pedestrians and thereafter it leads to temporary or permanent disability; thereby it is a foremost important condition for road users to identify the presence of electric vehicles while walking on the road. The study indicates that human safety is closely linked to the safe travel of pedestrians. The quiet nature of electric vehicles are difficult for a pedestrian to identify their presence on the road which is an alarming situation for the possibility of accidents, however, there is no significant difference between the people (road users) who travel a short or long distance in a day. It is the foremost requirement to recognize the level of consequence in the context of accidents to improve the global health.
Groundwater is the largest available reservoir of freshwater. But the rapid increase in the population and urbanisation, has led to over exploitation of groundwater which imposed tremendous pressure on global groundwater resources. Because of the hidden and dynamic nature of groundwater, it requires appropriate quantification for the formulation of groundwater planning and management strategies. The present study evaluates the efficacy of geospatial technology based Multi Influence Factor (MIF), Weight of Evidence (WofE) and Frequency Ratio (FR) technique to evaluate groundwater potential using a case study of basaltic terrain. The thematic layers influencing the groundwater occurrence viz. rainfall, slope, geomorphology, soil type, land use, drainage density, lineament density, and elevation were prepared using satellite images, hydrologic, hydrogeologic and relevant field data. Based on the conceptual frameworks of MIF, WofE and FR techniques these thematic layers and their features were assigned with appropriate weight and then integrated in the ArcGIS platform for the generation of aggregated raster layer which portray the groundwater potential zones. The results of validation showed that the groundwater potential delineated using MIF technique has a prediction accuracy of 81.94%, followed by WofE technique (76.19%) and FR techniques (71.43%). It is concluded that for evaluation of groundwater potential, the MIF technique is most reliable, followed by the WofE technique. The evaluated groundwater potential zones are useful as a scientific guide to identify the suitable location of wells and recharge structure in a cost-efficient way and also for the development of structured and pragmatic groundwater management strategies.
The assessment of the suitability of a region for urban development is crucial. It is necessary to make appropriate decisions for urban and regional development by considering essential criteria. Remote sensing and GIS techniques can be used to provide spatial information when assessing the suitability of a region. The aim of this study was to identify a suitable site for urban settlement in Tiruchirappalli. This study incorporates the GIS-based multi-influence factor (MIF) technique to identify land-use suitability for future development in Tiruchirappalli. Open land and agricultural land were the primary land-use classes in the study area. The evaluation process for land suitability analysis was based on 11 criteria including landuse/land-cover (LULC), road and rail network, industrial area, settlement, soil map, slope, population density, surface water bodies, groundwater level, and water quality. The site suitability map for urban settlement was divided into four regions: I— restricted, II—less suitable, III—moderately suitable, and IV—highly suitable. The results indicated that 15.46% of the study area was highly suitable for urban settlement, 41.38% was moderately suitable, 39.33% was less suitable, and 3.82% was restricted for urban settlement. Most of the suitable area was located in the central zone of the city, where the existing urban land was located. Receiver-operating characteristic (ROC) was applied to validate the model, and the area under the curve (AUC) was 0.837, suggesting that the model was efficient. The current study is thus proposed to be an effective tool for sustainable land-use planning and development in Tiruchirappalli.
In this paper, the application of hexagonal fuzzy multiple-criteria decision-making (MCDM) methodology for the site selection of electric vehicle charging stations is considered. In this regard, four factors and thirteen sub-factors have been taken into consideration for E-vehicle charging site selection. In this research, the geographic information system (GIS) has been incorporated with MCDM techniques. The fuzzy analytic hierarchy process (FAHP) is used to obtain a fuzzy weight of factors and sub-factors. MCDM tools fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) and fuzzy complex proportional assessment (FCOPRAS) have been used to rank the selected sites. A centroid-based method for defuzzification and distance measure between two hexagonal fuzzy numbers (HFN) has been developed for this paper. A practical example in Howrah, India, is considered to show the applicability and usefulness of the model. The results depict the suitability of the proposed research. Comparative and sensitivity analyses have been demonstrated to check the reliability, robustness and effectiveness of the proposed method.
This study explores the opportunities for irrigated agriculture in Khyber District of Former Federally Administered Tribal Area (FATA). Agriculture continues to be most important economic activity in Pakistan and former FATA is no exception. However, agriculture in the semi-arid Khyber District continues to suffer a decline due to excessive reliance on rainfall, which is drastically insufficient. Khyber District have large expanses of fertile land; however, farmers depend on unpredictable rain-fed agriculture, which has low productivity and food insecurity. There are a number of rivers in the region where great prospects exist for irrigated farming. Geographical information systems (GIS)-based multi-criteria analysis can be used to map areas with irrigation potential. The current study focused on different variables including soil texture, perennial rivers, land cover, topography, and dams. For this study, data was collected from diverse sources which include Google earth images, top sheets, land use land cover, soil texture map of FATA, and SRTM 30 Meter. The collected data was analyzed by using multi-criteria analysis in Arc GIS 10.2.2 by using MIF technique. The results of this research indicate that there is great potential for irrigated farming in the study area. Potential land for this purpose were divided into three categories, i.e., highly suitable which consisted of 678 km2, or 26% of the total area; 1044 km2 representing 40% were moderately suitable to irrigation; while the remaining 698 km2 representing 27% were considered least suitable for irrigation. Likewise, 204 km2 representing 7% were deemed unsuitable for irrigated agriculture mostly consisting of barren rocks. The results also illustrate how GIS as a tool can be used in the exploration of water resources in a scientific approach, thus making decision-making easier and more accurate.
As one of the internationally recognized solutions to environmental problems, electric vehicles feature zero direct emissions and can reduce dependence on petroleum. An increasing number of countries have attached importance to the electric vehicle and developed it, and it is predicted that it will become a main force in the transportation system. Hence, it is necessary to explore the factors that drive consumers to buy electric vehicles. This study analyzes the factors that influence the consumer’s intention to buy electric vehicles and tests the relationship between them, and intends to offer information for the formulation of policies designed to popularize electric vehicles in order to reduce carbon emissions from transportation. As a result, consumer attitudes are the most important factor influencing the intention to purchase electric vehicles. The greatest effect is found in this line: Brand Trust→Perceived Benefit→Attitude→Purchase Intention. This means that the brand can increase the consumer’s perceived benefit of electric vehicles, make consumers more attracted to electric vehicles, and influence their final purchase intention.
Motor vehicles are a major source of air pollutants in Beijing; reducing their emissions is important for achieving air quality standards and continued air quality improvement. Although the environmental benefit of electric vehicles (EVs) is acknowledged globally, the impact of replacing gasoline and diesel vehicles with EVs in megacities like Beijing has not yet been fully analyzed. This study examines how EV development impacts air quality improvement in Beijing. Based on the emissions of various vehicle types and the feasibility of their replacement, the reduction in air pollutants (CO, NOx, PM, and HC) emissions from EV deployment is estimated. Different EV development scenarios are simulated to investigate differences in air quality improvement. Through the implementation of the Clean Air Action Plan and the Pollution Prevention and Control Action Plan, Beijing has accelerated changes in vehicle structures and prompted rapid EV development. Vehicle electrification produces different environmental benefits in the use stage, depending on vehicle type. The electrification of large gasoline passenger cars has the highest environmental benefit, with an annual reduction of approximately 1.7 tons of air pollutant emissions per vehicle, followed by the electrification of diesel vehicles. To further improve Beijing’s air quality to achieve the goal of 15 µg/m3 PM2.5 by 2035, approximately 2.4 million fossil fuel vehicles must be replaced by EVs, including 84% of small passenger cars and 9% of logistics trucks. Given the small number of EVs currently in Beijing, vehicle electrification must be further promoted and accelerated over the next 15 years.
Flood is one of the most dangerous natural hazards in India due to their devastating effects in every sector. The present study was designed to develop flood-susceptible map using geospatial-based AHP method in Teesta River basin of West Bengal, India. At first, we selected ten flood factors such elevation, slope, TPI, SPI, NDVI, drainage density, mean annual rainfall, MNDWI, lithology, and distance from the river for developing flood susceptibility map. The most important flood influencing factors were elevation (27%), slope (19%), distance from the river (14%), and drainage density (14%). The obtained suitability map identified 11.99%, 13.90%, 21.48%, 23.93%, and 28.70% of study areas as very high, high, moderate low, and very low susceptible areas, respectively. The accuracy of flood susceptibility map was tested using ROC and AUC analysis. The obtained AUC value was 0.739, confirming the reliability of applied method in flood modeling. The finding of this study will be useful in preparing the effective flood management strategies.
Driving a conventional gasoline vehicle is an important polluting factor that causes environmental degradation. In order to reduce dependence on gasoline and its related environmental effects, electric vehicles are emerging as an alternative to conventional gasoline vehicles. Electric vehicles are both economical and environmentally friendly vehicles that draw their energy from rechargeable batteries in the car, which have many benefits such as reduced carbon emissions or pollution, cost-effectiveness, and less noise. The most important drawback of these vehicles is the problems with recharging. One way to deal with this problem is to build charge stations for electric vehicles. A suitable charge station for electric vehicles should also be located in a very precise place to get the most out of electric vehicles. Therefore, in this paper, an MCDA approach based on GIS for optimal site selection of charge stations has been conducted. A simple Hierarchical Analysis Process (AHP) is used to select the optimal locations for electric vehicle charge stations. The proposed method is applied in Kish (an island in the south of Iran) as the study area. The genetic optimization algorithm is applied to solve the optimization problem and is simulated in Matlab software. Using integrated modeling and mathematical optimization in a GIS operating system, the results showed that the proposed model can select suitable locations for charge stations for electric vehicles that can meet the demand for charging electric vehicles within a certain access distance. Also, the method of locating and determining the simultaneous capacity of solar sources and charge stations of electric vehicles and managing the charging process of vehicles in the network has been presented and suitable places for constructing charge stations of electric vehicles have been proposed.
Recent interest in planning a suitable charging infrastructure for electric mobility has motivated many studies. In this domain, attention towards key indicators of sustainable locations is crucial because they can provide informative signals to planning suitable charging station networks in cities according to multiple target attributes of sustainability. However, past literature has focused on “how to evaluate these key indicators of sustainable locations” by somewhat disregarding “which key indicators of sustainable locations were to be measured” based on the viewpoint of sustainability. This study presents a unified approach, identifying a long list of these indicators and then selecting a smaller set by applying an analytical hierarchy process, an algorithm, which aggregates weights and outcome marks, and a Monte Carlo simulation to an international survey. A relevant set of five overlapping key indicators of sustainable locations is designated by merging three different ranking methods. The results revealed a close semi-compensatory connection between transport and urban planning choices. These findings may support the planning of sustainable and suitable electric vehicle charging stations in cities according to a common set of key indicators of sustainable locations.
This paper proposes a method for electric vehicle charging station (EVCS) placement problem at the directional road segment (DRS) level for large urban road networks, which integrates a multi-criteria decision-making model with a new map matching technique called “segment-wise matching based on MRI”. The charging demand of DRS is estimated based on a novel prediction method which considers the arrival trips and the variation of charging demand for different trip purposes. Traffic attributes, charging demand attributes, and land price are incorporated into the TOPSIS model to determine the optimal EVCS placement. Finally, the proposed method is demonstrated using the road network of Xi'an in China as a case study. The results show the proposed method can be well applied to the EVCS placement problem at the DRS level for large-scale urban road networks. It is found that EVCS installation potentials of road segments approximately follow a normal distribution. The road segments with a high installation potential exhibit regional clustering characteristics due to the level of well-developed land use in the surrounding area. Sensitivity analyses suggest that it is important to include multiple criteria for modeling the EVCS placement problem and that traffic speed and arrival trips are key factors.
Urban areas account for around 70% of global GHG emissions, one-third of which is attributed to the transportation sector. Thus, decarbonising urban mobility is critical in the endeavours to mitigate climate change. Electric vehicles (EV) have potential to control global decarbonisation of transportation. India has air quality index of 141, which is amongst the highest in the world. Considering this troubling figure, government has initiated various schemes and initiative plans. This study reviews the progress and current status of EV adoption in India with regard to availability, manufacturing, charging infrastructure, commercial vehicle electrification and in-depth analysis of FAME-I and FAME-II schemes along with state level initiatives. This research explores the current state of EV adoption through the integrative perspective of financial resources, infrastructure and technology availability highlighting various complications. Based on these challenges, the study proposes points of improvment in each fragment such as policies, infrastructure,electricity demand and sustainable source of energy. A roadmap is recommended of primary factors that should be priortised for effective EV adoption.
Electric Vehicles (EVs) are still not a preferable choice among the consumers of vehicular world. It has now become necessary to showcase the merits of EVs to the consumers. If compared to the nonhilly areas, people in hills are still reluctant toward adoption of EVs due to various constraints as mentioned in this article. Several factors affect the desirability of EVs, including technical and customer-related factors. This work also proposes a model to help customers choose the most suitable EV by adjudging vehicles concerning multiple factors. During the judging process, extra care is taken to include
factors defining the viewpoint of the general customer while purchasing the EV. Distinctly, two sets of criteria, one highlighting the technical specifications of the EV and the other exclusively defining the customer’s perspective of it, including features like cost, warranty, charging facility, etc., are taken up to rank the six most popular EV models in India using a Fuzzy-based Multi- Criteria Decision-Making (MCDM) model. Here, the Quality Function
Deployment (QFD) model is used to tackle the weightage of the criteria based on customer’s feedback. The outcome of this work and the evaluation process can act as a quick guideline for any arbitrary customer who plans to make a new purchase of an EV. Hyundai Kona electric scored 0.223 the highest, making it the most suitable choice, and Mahindra E20 plus scored 0.121, making it the least suitable choice among the list of EVs chosen in the study for the Indian market. An additional case study segment in this article
focuses on the market penetration issues that the EVs may likely face on a hilly terrain in the perspective of energy consumption per trip and the customer woes that may arise due to terrain.
Location selection of charging stations of electric vehicles (EVs) contributes to long-term sustainable urban development. This study proposes a hybrid approach integrated with the Geographical information system (GIS) and Bayesian network (BN) to deal with the location selection problem for EVs. GIS serves for capturing spatial and geographical data, which provides dynamic and visual information for selecting charging sites. BN is employed to process various criteria and demonstrate the cause-effect relationship in alternative site selection. A BN model consisting of nine criteria from three aspects is established to determine the most suitable locations for charging stations of EVs. A total of ten alternative locations in Singapore is used to verify the applicability and effectiveness of the developed hybrid approach. Results indicate that (1) Criteria, including the number of MRT stations, household units, and charging efficiency, are identified as the most sensitive factors to the location selection; (2) The transportation efficiency has the strongest linkage with the location selection (with an average value of the strength of 0.445), revealing that the transportation efficiency is more important than the social and economic efficiency. The novelty of this research lies in the development of the hybrid GIS-based BN approach that is more accurate and stable under noise interruption compared to the traditional decision-making method (e.g., TOPSIS). The developed approach can be used as a decision tool to identify the major contributing factors and update the optimal decisions given new observation data in GIS in an automatic manner.
It is undoubtedly that the environmental and economic benefits will increase by integrating electric vehicles, which are the vehicles of the future, with the car-sharing system. The problem of determining the locations of electric vehicle charging stations (EVCS) and service areas of the car-sharing system appear with the integration. Environmental and energy concerns are the biggest driving factor behind electric car-sharing systems. In this study, Geographic Information Systems (GIS) based fuzzy multi-criteria decision making (MCDM) method is proposed for the solution of site selection problems of electric car-sharing stations (ECSS). To do so, a three-step solution methodology is developed: (i) determining 20 sub-criteria and weighting the criteria with fuzzy Analytic Hierarchy Process (f-AHP), (ii) obtaining a suitability map for potential ECSS via GIS, (iii) sorting the performance levels of ECSSs assigned according to the suitability map by Elimination and Choice Translating Reality (ELECTRE). The proposed methodology is applied for Istanbul, a metropolitan city in Turkey as a case study. The results show that the most suitable areas for ECSSs in the European and Anatolian side, Istanbul is an intercontinental city, are the south-east part and south-western part, respectively.
Vehicular traffic noise is inevitable problem having environmental impact in mega cities like Mumbai. In present study, an effort has been made to predict present and future noise as a function of vehicular growth. Further, a new approach have been proposed to estimate environmental noise capacity for the city. Noise was pedicted by classifying vehicles as light and heavy based on thier weights and used as a input to calixto model. The number of vehicles for 2018 were estimated using annual growth rate. The predicted urban noise from Calixto model were validated with real time monitoring results. The analysis revealed that there is variability in traffic flow pattern across the different roads in Mumbai. Location S2, which is in between Kurla and Ghatkopar contributes highest share of vehicle movement resulting in higher noise level (85 dB (A)) among the studied locations. Environmental noise capacity for all the locations are greater than zero except for location S4 (Chembur). The calculated and observed noise levels are found to be in agreement with R² value of 0.795 and 0.853 for weekday and weekend respectively. This study will be useful for the policy makers for planning the strategies to mitigate the impacts of increasing urban noise. This shall be a breakthrough in formulating policy especially for a cities like Mumbai having mixed category zones in each location and may be helpful in defining a new approach to estimate ambient noise levels based on land use pattern.
Electric vehicles have gained rapid development in recent years, and many shaped electric vehicle charging stations (EVCS) have been built in China. However, with the increase of the number and scale of EVCS, the economic problems of EVCS operation are becoming more and more prominent, and there are many problems in their operation, all of which affect the economic and social benefits of EVCS. In order to improve the economic operation of EVCS, this paper adopts Decision Making Trial and Evaluation Laboratory-Interpretative Structural Modeling (DEMATEL-ISM) method to identify and analyse its influencing factors. According to the opinions of experts and EVCS enterprises, the influencing factors system of economic operation of EVCS is constructed by Delphi method, including four aspects of planning, external environment, management and technology. Based on the analysis results of multilevel hierarchical structure model, the influencing factors of EVCS economic operation can be divided into four levels. Among them, the top-level factors include charging price, gasoline price, electric vehicle battery, reliability of power supply, and spare parts management. The factors in the second layer include charging monitoring system and safety management. The third level factors include charging station address, charging station scale, regional power grid situation, government policy, technical supervision management, operation data analysis and management, personal training management. The third level elements directly affect the second level factors and have an impact on the first level factors through the second level factors. Furthermore, it is also found that the number of electric vehicles is the deepest factor and the most basic segment which affects the economic operation of EVCS and the other factors. This method is feasible and can be utilized to quantitatively analyse the influencing factors of economic operation of EVCS.
The purpose of this study is to explore the factors that affect adoption intention of electric vehicles (EVs) in India. An integrated model using Unified Theory of Acceptance and Use of Technology (UTAUT) was developed to forecast customer's adoption intention towards EVs in India using perceived risk as the predictor and environmental concerns and government support as the moderators. Quantitative data from 284 customers was analyzed using hierarchical linear regression analysis and verified with necessary conditions analysis. The findings show that performance expectancy and facilitating conditions positively affect adoption intention of EVs, whereas perceived risk negatively affects adoption intention of EVs. Government support was found to moderate the relationship between perceived risk and adoption intention such that it reduces the inverse relationship of perceived risk on adoption intention. The results validate UTAUT by studying it in the context of EV's adoption in India and adds to the literature on psychological traits affecting adoption intention of new products. Implications from results for policy and practice in terms of specific measures that could be taken by automobile firms and governments to promote adoption based on results are also discussed.
Electric vehicles emerge as the possible strategy for decarbonization and green transportation due to social demand. Researchers have made multiple efforts and initiatives as the demand surge for sustainable development in the electric vehicle industry. This study analyzes the relevant research of the industry, thereby explores electric vehicle industry development trends with a scientometrics-based data evaluation system, where three key topics are detected: “Vehicle Exhaust Emissions”, “Climate Change”, and “Integration”. The results are visualized in the view of clusters, timeline, and time zone to explore the dynamic direction and future trends of the industry. Further trend detection and forward-looking analysis suggest the significance of stakeholders and their interconnection. In response to the significant challenges in sustainable development, this study proposes the stakeholder engagement system in a comprehensive perspective. The system firstly reveals the driving forces behind the industry development, that is how different motivation and strategies orientate the industry towards sustainability. Then, it furtherly analyzes the commitments and efforts needed from multiple stakeholders, through policy implications, leading factors on demand side, and technology innovation requirements on supply side. The stakeholder engagement system may contribute to stakeholder synergy and resource optimization hence for electric vehicle industry sustainable development and novel ideas to other relevant research fields.
The use of electric vehicles has been increasing rapidly in recent years. Hence, determining the location of charging stations is a strategic decision. Managements may consider relocating charging stations as demand may change over time. In order to reflect the uncertainty in demand, it is appropriate to use fuzzy expressions. The demands of customers at present time can be calculated according to the criteria determined by using the fuzzy evaluations of decision makers. In this study, the single facility relocation problem for time-dependent demand is examined on finite time horizon. Rectilinear distance is considered. Demands of locations are calculated according to the criteria set by decision makers using cylindrical neutrosophic single-valued fuzzy expressions. This methodology is applied to the problem of minimum cost location of electric charging stations. The time-dependent demands of customers are determined with the help of cylindrical neutrosophic single-valued numbers. The minimum cost of relocating the charging station in a ten-year period is investigated. This application is intended to guide future facility relocation approaches.
India has been falling behind other countries in the deployment of electric vehicle (EV), as it has no explicit policy or strategy. This contrasts with the USA, China, Norway and Germany, who have offered substantial subsidies and incentives to upgrade to EVs, to minimize air pollution and fossil fuel consumption. The main purpose of this paper was to review the policies, strategies, and technical considerations for developing EVs, by analyzing both the Indian EV market and the global evolution of EVs. This study also considered the development and research status of EVs in India. In addition, the current deployment of EVs in India, together with the challenges and opportunities within this sector, was explored and a strength weakness opportunity and challenges (SWOC) analysis performed. This study will encourage policy makers, government and businesses to incentivize the deployment of EVs in India in order to reduce greenhouse gas emissions. It was concluded that the Indian government should provide more research funds for the development of both EVs and the charging infrastructure. Also, central government could play a major role in coordinating the activities of the state and EV related businesses, to support the emerging Indian EV sector.
Sustainable site selection for electric vehicle charging station (EVCS) is a significant process in the promotion of electric vehicle system development. The assessment and selection of suitable EVCS site is a very critical decision, involving complexity due to the presence of several associated criteria. Furthermore, uncertainty is an inevitable component of the information in the decision‐making procedure and its significance in the selection process is relatively high and needs to be cautiously measured. Single‐valued neutrosophic set (SVNS) is one of the valuable and flexible tools for handling such type of uncertain information arising in multi‐criteria decision‐making (MCDM) applications. Thus, the objective of this study is to introduce novel single‐valued neutrosophic information‐based additive ratio assessment (ARAS) approach for evaluating and prioritizing the sustainable EVCS sites. In this method, novel single‐valued subjective and objective weighted integrated approach (SVN‐SOWIA) is developed to compute the criteria by aggregating the objective weights resulted from a similarity measure‐based procedure and the subjective weights given by the experts. For this purpose, an innovative similarity measure is proposed for SVNSs. To display the performance of the present methodology, a computational study of EVCS sites evaluation is conferred under single‐valued neutrosophic environment. Comparative and sensitivity analyses are further performed to verify the strength of the developed approach. The outcome illustrates EVCS site EvUrjaa—Electric Vehicle Charging Station is the most optimal EVCS site in Indore region, India. Also, the environmental (0.324) and social (0.273) criteria are more important than technological (0.236) and economical (0.167) criteria in assessing the EVCS sites. The sensitivity analysis outcomes signify the EVCS option EvUrjaa—Electric Vehicle Charging Station always acquires its highest ranking in spite of how sub‐criteria weights fluctuate. The outcome of this study indicates that the developed approach can suggest more realistic performance under uncertain environment and therefore, provides a wide range of applications.
The optimal location of electric vehicle charging station (EVCS) will promote the rapid development of the electric vehicle (EV) industry. Generally, EVCS location selection is treated as complex uncertain multi-criteria decision making (MCDM) problem because of the existence of many quantitative and qualitative influencing factors. Moreover, uncertainty is usually occurred in EVCS location selection problem and Fermatean fuzzy set (FFS), as an expansion of orthopair fuzzy set, can effectively handle the ambiguity by reducing human intervention. Thus, the aim of the current study is to design an integrated decision making method for solving multi-criteria EVCS location selection problem under FFS context. This method is based on Multi-Objective Optimization based on the Ratio Analysis with the full multiplicative form (MULTIMOORA) approach, Maximizing deviation method and Einstein aggregation operators within Fermatean fuzzy environment. At the same time, the criteria weights are determined through the Maximizing deviation method. For this, we introduce a divergence measure for FFSs. To aggregate the decision information, we propose some novel Einstein operations for FFS. In light of these operational laws, we further suggest some Fermatean fuzzy Einstein aggregation operators and their enviable characteristics. To illustrate the potentiality and usefulness of the present methodology, we carry out an illustrative study of EVCS location selection problem with FFS setting. Comparing the present MULTIMOORA framework with the extant methods confirms the strength of the obtained outcomes. The findings conclude that the introduced method is more useful and well-consistent with extant methods.
The primary aim of this study is to provide insights into different low-carbon hydrogen production methods. Low-carbon hydrogen includes green hydrogen (hydrogen from renewable electricity), blue hydrogen (hydrogen from fossil fuels with CO2 emissions reduced by the use of Carbon Capture Use and Storage) and aqua hydrogen (hydrogen from fossil fuels via the new technology). Green hydrogen is an expensive strategy compared to fossil-based hydrogen. Blue hydrogen has some attractive features, but the CCUS technology is high cost and blue hydrogen is not inherently carbon free. Therefore, engineering scientists have been focusing on developing other low-cost and low-carbon hydrogen technology. A new economical technology to extract hydrogen from oil sands (natural bitumen) and oil fields with very low cost and without carbon emissions has been developed and commercialized in Western Canada. Aqua hydrogen is a term we have coined for production of hydrogen from this new hydrogen production technology. Aqua is a color halfway between green and blue and thus represents a form of hydrogen production that does not emit CO2, like green hydrogen, yet is produced from fossil fuel energy, like blue hydrogen. Unlike CCUS, blue hydrogen, which is clearly compensatory with respect to carbon emissions as it captures, uses and stores produced CO2, the new production method is transformative in that it does not emit CO2 in the first place. In order to promote the development of the low-carbon hydrogen economy, the current challenges, future directions and policy recommendations of low-carbon hydrogen production methods including green hydrogen, blue hydrogen, and aqua hydrogen are investigated in the paper.
The transport sector has been one of the largest source of carbon emission and urban air pollutants. The research on the coordinated development of pollutant and carbon emission reduction in transport industry is helpful to the realization of urban pollutant prevention and carbon emission reduction, especially in big cities. Thus, a multi-period bottom-up vehicle development mathematical model is proposed to analyze the technology development path, emission path and energy structure adjustment path, and the synergistic benefits of carbon dioxide (CO2) emission reduction under a expected air pollution emission standard. Four pollutants, carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), and particulate matter (PM), generated from the vehicle are considered in this model. Then, the proposed model is used to analyze the related vehicle structure and energy consumption under the expected emission standards for Beijing during 2020 and 2035. The technology development path, emission path and energy structure adjustment path are examined, and the synergistic benefits of CO2 emission reduction are also studied. Some important implication are found as follows: (1) Even with the goal of environmental pollution control only, new energy vehicles will have an explosive growth period, starting from about 2025. (2) Strict air pollution emission policies do not always lead to the rapid development of new energy vehicles before 2025. (3) The four main pollutants show different levels of synergistic effect among which CO on HC and NOx on PM are obvious, respectively. (4) Even under the control of the air pollution policy, the synergistic effect to CO2 emission reduction is also obvious. Compared to the baseline case, the reduction benefit from the MILD and STRICT environmental policies are 30 and 70 million yuan, respectively.
Critical infrastructure systems, including transportation, energy, and water/wastewater, lose their essential functionality when exposed to hazards. The internal complexity of lifeline networks and the cascading interdependency that exists among multiple lifeline networks further augment the impact of external perturbations. To help the networks recover and adapt when adverse events occur, resilience must be built into the system. However, translation of resilience to an operational paradigm remains a challenge because the underlying complexity of the large systems must be deciphered first to translate research into decisions and policies. Moreover, in the context of climate and weather extremes, internal variability dominates in the stakeholder relevant near-term (0–30 years). Climate model simulations attempt to capture the inherent variability in climate systems through multiple initial condition ensembles. Hence there is a need to integrate tools for decision-making uncertainty to translate the information available at disparate spatiotemporal scales with varying credibility to stakeholder relevant insights. In addition, the perspective would need to consider a holistic framework that embraces the visualization of potential hydrometeorological threats in addition to critical functions and cascading failures across infrastructure sectors. In this chapter, we discuss the gaps using a unifying lens for climate and weather extreme stressors and stressed systems (infrastructure lifelines). Furthermore, we illustrate a solution framework in the context of the resilience of transportation networks under exacerbated stress from precipitation extremes in changing climate scenarios.
The groundwater potential map is an important tool for a sustainable water management and land use planning, particularly for agricultural countries like Vietnam. In this article, we proposed new machine learning ensemble techniques namely AdaBoost ensemble (ABLWL), Bagging ensemble (BLWL), Multi Boost ensemble (MBLWL), Rotation Forest ensemble (RFLWL) with Locally Weighted Learning (LWL) algorithm as a base classifier to build the groundwater potential map of Gia Lai province in Vietnam. For this study, eleven conditioning factors (aspect, altitude, curvature, slope, Stream Transport Index (STI), Topographic Wetness Index (TWI), soil, geology, river density, rainfall, land-use) and 134 wells yield data was used to create training (70%) and testing (30%) datasets for the development and validation of the models. Several statistical indices were used namely Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity (SST), Specificity (SPF), Accuracy (ACC), Kappa, and Receiver Operating Characteristics (ROC) curve to validate and compare performance of models. Results show that performance of all the models is good to very good (AUC: 0.75 to 0.829) but the ABLWL model with AUC = 0.89 is the best. All the models applied in this study can support decision-makers to streamline the management of the groundwater and to develop economy not only of specific territories but also in other regions across the world with minor changes of the input parameters.
This study involves the demarcation of Groundwater Potential Zones (GWPZ), which has been produced by Multi-Influencing Factors (MIF) and Analytic Hierarchy Process (AHP) with the aid of Remote Sensing and Geographic Information System (GIS). Eleven influencing factors, viz.-aquifer thickness, geology, rainfall, lineament density, soil texture, slope, elevation, pond density, drainage density, NDVI, and land use and land cover were used to delineate GWPZ maps. GWPZ map prepared by the MIF technique classified into low, medium, and high, covering 44.05, 32.82, and 23.13 % of the test area. Contrary to this, the GWPZ map obtained by the AHP technique classified into the same categories, low, medium, and high, covering 41.41, 34.32, and 24.28 % of the test area. Using Kappa statistics, accuracy assessment of the GWPZ maps has been done for both the MIF technique and AHP technique with the agreement value of 0.754 and 0.702. In this study it has been found that groundwater potentiality is not satisfactory in the northwestern part of the test region. Therefore, to use it wisely, we may encourage the use of pond irrigation, river lifting, rain water harvesting.
An integrated multi-criteria decision-making (MCDM) method is developed through the linguistic entropy weight (LEW) method and fuzzy axiomatic design (FAD) to select a suitable site for an electric-vehicle charging station (EVCS). Based on expert opinions from different fields, a literature survey, and on-site investigation, an evaluation index system for EVCS site selection is constructed from a sustainable perspective; the indicator system has 13 sub-criteria, including technical, economic, social, environmental, and resource ones. Next, outcomes are presented from the criteria performances and weights of different alternatives having been evaluated by a panel of five technical, economic, social, ecological, and urban-planning experts. Finally, criteria weights are determined by the LEW method, and the most suitable EVCS site is determined by the FAD method. Moreover, a LEW–FAD integrated analysis framework is constructed, and the process for calculating the optimal EVCS location is given. To assess the stability and robustness of the proposed method, sensitivity and comparative analyses are conducted. The results of the sensitivity analysis show that the ranking of alternatives is unaffected by changes in the functional requirements of the criteria but affected considerably by changes in the criteria weights. The advantages of the proposed method are highlighted in terms of stability and reliability by comparisons with three MCDM methods applied in previous studies, and the effectiveness of the proposed method is verified. The results show that the application of LEW method and FAD in EVCS site selection is robust. Therefore, the evaluation criteria and method proposed in this paper are also suitable for other rapidly developing or emerging economies.
The construction of fast electric vehicle (EV) charging stations is critical for the development of EV industry. The integration of renewable energy into the EV charging stations comprises both threats and chances. A successful and reasonable capacity configuration and scheduling strategy is beneficial and significant. This paper studies the optimal design for fast EV charging stations with wind, PV power and energy storage system (FEVCS-WPE), which determines the capacity configuration of components and the power scheduling strategy. Firstly, an EV charging load simulation model considering demand response is built, which dynamically modified charging expectation under time-of-use electricity price. Secondly, based on the system design, a multi-objective optimization model is proposed with minimum objectives of cost of electricity and pollution emissions. Then, this model is solved by a hybrid optimization algorithm which combines multi-objective particle swarm optimization (MOPSO) algorithm and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Finally, the proposed optimization framework is applied to a case in Inner Mongolia, China. A scenario analysis is conducted and concludes that the renewable energy supplies, the connection with utility grid and demand response can help improve the performance on optimization objectives. A sensitivity analysis is also performed to verity the model’s effectiveness. In addition, the proposed method is compared with simulated annealing and genetic algorithm to show its faster computation speed and higher solution quality.
The integration of photovoltaic (PV) power generation system and electric vehicle (EV) charging station can effectively promote the local consumption of renewable energy and reduce the indirect carbon emissions of EV. This paper aims to provide a practical model for location decision of PV charging station (PVCS) which combines geographic information system (GIS) with multi-criteria decision making (MCDM) methods. To verify the feasibility and practicality, an empirical study was conducted in Beijing. First, seven suitable areas were selected preliminarily by suitability analysis of GIS. This stage focused on traffic flow and road distribution. Second, MCDM methods were used to further evaluation. At this stage, a comprehensive evaluation index system of natural, economic, technical and social criteria was established. Interval number, triangular fuzzy number and hesitant fuzzy linguistic term set were utilized to collect and describe evaluation information. Then, the subjective and objective weights of the criteria were calculated by best-worst method and mixed information entropy method, respectively. Finally, the TODIM (an acronym in Portuguese of interactive and MCDM) method was used to rank the alternatives. The ranking results illustrated that alternative sites A3 and A7 are outstanding. Moreover, dual sensitivity analysis and comparative analysis proved the stability and reliability of the model. Scenario analysis expanded the application scope of TODIM method, which also showed that it is necessary to express decision preferences by setting different recession coefficients. This study can provide support for the layout of PVCS in urban, and enrich the application fields of GIS and MCDM methods.
Safety seat belt usage has been a great interest to the transportation community. Understanding factors that influence driver’s decision of wearing a safety seat belt or not is essential in determining ways to enhance safety seat belt usage rate. A modeling approach is made to observe the trend of seat belt usage in Mumbai city and to understand the effect of vehicle type, ownership type, driver’s sociodemographic, and environmental characteristics on safety seat belt usage in Mumbai City. Data were collected by roadside observational surveys at various locations in Mumbai during the years 2015 through 2018. The time series model estimate confirms declining trend of drivers not wearing safety seat belt. When vehicles are disaggregated into different build types, buses are found to be associated with no use of safety seat belt as compared to other type of vehicles, and even male drivers follow the same trend in the city. By using random parameter logit model unobserved heterogeneity was captured among individuals. Findings can be used by policymakers to develop intervention strategies to increase seat belt usage in Mumbai and other cities having similar traffic characteristics and social environment features.
The biggest change will occur in taxis which have an important share in transportation along with the dissemination process of electric vehicles (EV). Therefore, charging infrastructure problem must be solved for electric taxis (ETs). It is aimed to solve the site selection problem of electric taxi charging station (ETCS) for Istanbul, a metropolitan city in this study. A four-step solution approach is developed: (i) determination of six main and 25 sub-criteria, (ii) weighting of evaluation criteria using fuzzy Analytic Hierarchy Process (fuzzy AHP), (iii) Performing spatial analysis of criteria via Geographical Information System (GIS) and assigning of ETCSs, (iv) ranking of assigned ETCSs using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) accordance with performance values. The results show that, in Istanbul, it is seen that the southeast part of the European side and the southwestern part of the Anatolian side are more suitable for ETCS. The proposed methodology approach provides a more accurate solution for high degree of uncertainty problems.