Quang-Thanh Bui

VNU University of Science | HUST · Center of Applied research in Remote sensing and GIS

This paper explores the use social media data from Twitter to capture perceptions of neighbourhood characteristics, in relation to gentrification. It does this by defining a rudimentary lexicon of words associated with gentrification which was used to calculate gentrification scores for geo-located tweets. These were then interpolated to create the...

Evaluating groundwater potential is critical for the socioeconomic development of Vietnam. This research aims to assess the underground water potential in the country’s Mekong Delta using the machine learning (ML) such as support vector machines (SVM), CatBoost (CB), K-nearest neighbors (KNN), random forest (RF) and AdaBoost (ADB). The problem of e...

Floods are arguably the most impactful of natural hazards. The increasing magnitude of their effects on the environment, human life, and economic activities calls for improved management of water resources. Flood susceptibility modeling has been used around the world to reduce the damage caused by flooding, although the extrapolation problem still...

Many cities are facing challenges caused by the increasing use of motorised transport and Hanoi, Vietnam, is no exception. The proliferation of petrol powered motorbikes has caused serious problems of congestion, pollution, and road safety. This paper reports on a new survey dataset that was created as part of the Urban Transport Modelling for Sust...

Groundwater resources are required for domestic water supply, agriculture, and industry, and the strategic importance of water resources will only increase in the context of climate change and population growth. For optimal management of this crucial resource, exploration of the potential of groundwater is necessary. To this end, the objective of t...

Flood prediction is an important task, which helps local decision-makers in taking effective measures to reduce damage to the people and economy. Currently, most studies use machine learning to predict flooding in a given region; however, the extrapolation problem is considered a major challenge when using these techniques and is rarely studied. Th...

Landslides lead to widespread devastation and significant loss of life in mountainous regions around the world. Susceptibility assessments can provide critical data to help decision‐makers, for example, local authorities and other organizations, mitigating the landslide risk, although the accuracy of existing studies needs to be improved. This stud...

The effects of flooding can be very serious, especially in developing countries, where rapid urbanization and socio-economic development increases risk. Reliable information is crucial to support decision-makers develop appropriate strategies to reduce flood risk. This article aims to develop a framework for assessing flood risk and adaptive capaci...

Flood models based on traditional hydrodynamic modeling encounter significant difficulties with real-time predictions, require enormous computational resources, and perform poorly in data-limited regions. The difficulties are compounded as flooding worldwide worsens due to the increasing frequency of short-term torrential rain events, making it mor...

Natural hazards constitute a diverse category and are unevenly distributed in time and space. This hinders predictive efforts, leading to significant impacts on human life and economies. Multi‐hazard prediction is vital for any natural hazard risk management plan. The main objective of this study was the development of a multi‐hazard susceptibility...

Changes to the coastline or shoreline arise from the water's dynamic interaction with the land surface, which is triggered by ocean currents, waves, and winds. Various methods have been proposed to identify and monitor coastlines and shorelines, but their outcomes are uncertain. This study proposes indicators for identifying coastlines and shorelin...

Soil salinization is considered one of the disasters that have significant effects on agricultural activities in many parts of the world, particularly in the context of climate change and sea level rise. This problem has become increasingly essential and severe in the Mekong River Delta of Vietnam. Therefore, soil salinity monitoring and assessment...

The objective of this study is the development of a state-of-the-art method based on long short-term memory (LSTM), support vector machine (SVM), and random forest (RF) to predict the streamflow in the Mekong Delta in Vietnam, an area crucial to Vietnam's food security. Water level and flow data from 2014 to 2018 at the Tan Chau station and Can Tho...

The length of global coastline is about 356 thousand kilometers with various dynamic natural and anthropo-genic. Although the number of studies on coastal landscape categorization has been increasing, it is still difficult to distinguish precisely them because the used methods commonly are traditional qualitative ones. With the leverage of remote s...

Understanding the negative effects of climate change and changes to land use/land cover on natural hazards is an important feature of sustainable development worldwide, as these phenomena are inextricably linked with natural hazards such as landslides. The contribution of this study is an attempt to develop a state-of-the-art method to assess the e...

The Global Positioning System (GPS) is satellite-based, with receiving equipment worldwide utilizing geographic positioning satellites in Earth orbit. The system is unaffected by the radio positioning system, so it provides highly accurate three-dimensional positioning, velocity, and time data to users. In this paper, Alishan Township, Chiayi Count...

Properly choosing hyper‐parameters improves machine learning models' performance and reduces training time and resource requirements. In this study, we investigated the uses of the Bayesian optimization algorithm for hyper‐parameter searches of two classifiers, namely LightGBM and XGBoost. The models were verified with a dataset from Vietnam, inclu...

This study’s main objective is to propose a hybrid machine learning model based on a gradient boosting algorithm named LightGBM and an artificial ecosystem-based optimization to improve the accuracy of forest fire susceptibility assessment. Four hundred twenty-six historical forest fires from the NASA portal and thirteen conditional factors includi...

The Modifiable Areal Unit Problem or MAUP is frequently alluded to but rarely addressed directly. The MAUP posits that statistical distributions, relationships and trends can exhibit very different properties when the same data are aggregated or combined over different reporting units or scales. This paper explores a number of approaches for determ...

This study aims to develop a comprehensive approach including an analysis of the relationships between flood susceptibility and land-use change, based on the relevance vector machine (RVM) and coyote optimization algorithm (COA) models, applied to Gianh River watershed, Quang Binh province, Central Vietnam. Standard statistical indices, e.g. area u...

Agricultural land abandonment due to floods has become a significant global problem, causing multiple environmental issues, deteriorating rural landscapes, and impacting the socioeconomic conditions of farmers. Although research on agricultural abandonment has addressed different spatial scales, very few studies have focused on Southeast Asian coun...

The crucial importance of land cover and use changes and climate changes for worldwide sustainability results from their negative effects on flood risk. In a watershed, a particularly important research question concerning the relationship between land use and climate change and the flood risk is the subject of controversy in the literature. This s...

Flood effects are very serious, especially in developing countries where they are at high risk due to urbanization and socio-economic development. Reliable information is crucial to support decision-makers or planners to develop appropriate strategies to reduce flood risk. This article aims to develop a theoretical framework for assessing flood ris...

Machine learning applies predominantly to the classification of the satellite images, aerial photo, unmanned aerial vehicle (UAV) data, point clouds with considerable achievements. However, the dynamic and complex structures of land surface prevent accurate land cover segregation through built-in models, and there is a crucial need to investigate n...

Saltwater intrusion is a basic concern in many parts of Vietnam relative to long-term dependable water supplies. It affects many sides of human life and the ecosystem. Remote sensing is a useful tool for saltwater intrusion monitoring. In this study, we proposed an integrated approach to estimate EC (electrical conductivity) value from multitempora...

Floods are the most dangerous natural disasters globally, occurring on a large scale, and cause significant economic and environmental damage. Therefore, determining flood susceptibility is essential to reducing the flood effects on human lives and materials. The main objective of this research is to develop a novel hybrid algorithm, through combin...

Nationwide dental health surveys are crucial for providing essential information on dental health and dental condition-related problems in the community. However, the relationship between periodontal conditions and sociodemographic data has not been well investigated in Vietnam. With data from the National Oral Health Survey in 2019, we performed s...

In regular convolutional neural networks (CNN), fully-connected layers act as classifiers to estimate the probabilities for each instance in classification tasks. The accuracy of CNNs can be improved by replacing fully connected layers with gradient boosting algorithms. In this regard, this study investigates three robust classifiers, namely XGBoos...

With population growth, the demand for land resources is expected to increase significantly in the coming decades. Maintaining the integrity of soil distribution requires a remarkable amount of work to deal with agricultural extension. Salinity intrusion monitoring is a crucial process, which directly affects sustainable development, especially in...

This paper investigates a novel hybrid model that employs Henry’s gas solubility optimization algorithm to search for adaptive weights of a deep neural network for a landslide susceptibility application. The model is trained using 13 features from a case study site in Viet Nam, including topographically derived data, satellite-derived indexes, phys...

Landsat and Sentinel-2 are two freely accessible satellite data that are relevant for global land cover monitoring. However, the uses of the latter data set are growing because of its higher spatial resolutions and the availability of benchmark data sets for deep learning applications. In this study, we integrate a style transfer (perceptual loss e...

Context The late 20th and early twenty-first centuries saw massive urbanizations across the world, leading considerate changes in land-cover/land-use, and posing significant threats to UNESCO recognized heritage. Objectives The Complex of Huế Monuments, one of World Heritage Site in the centre of Vietnam, is now facing such challenges. This paper...

Flood risk is a significant challenge for sustainable spatial planning, particularly concerning climate change and urbanization. Phrasing suitable land planning strategies requires assessing future flood risk and predicting the impact of urban sprawl. This study aims to develop an innovative approach combining land use change and hydraulic models t...

This article investigates the use of the galactic swarm optimization algorithm in searching for parameters of a convolutional neural network for flood susceptibility mapping. Ha Giang province, the mountainous area of Vietnam, was chosen as a case study because of the frequent occurrence of floods. From this study area, 11 predictor variables and h...

The Linbeiken area is located in the village of Pingding, Taiwan. Since the Mindulle and Aere Typhoons in 2004, and as a result of the landslide triggered by the continuous heavy rainfall on 9 June 2006, there has been a persistent collapse of side slopes in the area. This paper describes the equipment that was installed to collect on-site topograp...

Cloud detection is a significant task in optical remote sensing to reconstruct the contaminated cloud area from multi-temporal satellite images. With the rapid development of machine learning techniques, especially deep learning algorithms, in general, propose a possibility to automatically detect cloud over a large area in optical remote sensing d...

The importance of studying coastal areas is justified by their resources, ecosystem services, and key role played in socio-economic development. Coastal landscapes are subject to increasing demands and pressures, requiring in-depth analyses for finding appropriate tools or policies for a sustainable landscape management. The present study addresses...

Land consolidation is an effective solution for the hindrances in agricultural production and rural development caused by land fragmentation. In the Red River Delta of Vietnam, where land is still highly fragmented, the application of land consolidation is required. By using a bottom-up approach, the paper aims to clarify the effect of land consoli...

Background Advances in earth observation and machine learning techniques have created new options for forest monitoring, primarily because of the various possibilities that they provide for classifying forest cover and estimating aboveground biomass (AGB). Methods This study aimed to introduce a novel model that incorporates the atom search algori...

Convolutional neural network (CNN) is a widely used method in solving classification and regression applications in industries, engineering, and science. This study investigates the optimizing capability of a swarm intelligence algorithm named moth flame optimizer (MFO) for the optimal search of a CNN hyper-parameters (values of filters) and weight...

Although coastal classification has been attended in recent years, it is still a complicated problem in quantitative geomorphological and hydrological sciences. Nowadays, the integration of deep learning in remote sensing and GIS analysis can quickly classify and detect different characteristics on both land and sea. Therefore, the authors proposed...

This study aims at investigating the balance between exploration and exploitation search capability of a newly developed Salp swarm optimization algorithm (SSA) for fine-tuning parameters of a three-hidden-layer neural network. The landslide study was selected as a thematic application, and a mountainous area of Vietnam was chosen as a case study....

Coastal areas are very important due to the ecosystem services provided to the inhabitants. However, these advantages have resulted into an increasing attraction, especially in the recent centuries. The human pressure determined numerous impacts on the ecosystems and ultimately on the safety and welfare of the coastal inhabitants, aggravated by the...

This study proposed and compared several novel hybrid models that combined swarm intelligence algorithms and Deep Learning Neural Network for flood susceptibility mapping. Lai Chau, a province in the northwest mountainous region of Vietnam was chosen as a case study since it had recently undergone severe flashflood in 2018. For this purpose, numeri...

Tourism is one of the smokeless industries that has been developing rapidly, opening up many job opportunities as well as socio-economic development for many countries around the world. In Vietnam, the role of the tourism industry in the development of the country has been well recognized and has received early investment attention from the Party a...

In remote sensing, Fuzzy C-Means clustering (FCM) is a robust method in determining membership grades of a pixel belonging to 1 or more classes. This paper proposes a novel approach by using the social spider optimization (SSO) algorithm in solving the search for optimal cluster centers in FCM. Hanoi, the capital of Vietnam, was chosen as a case st...

Adaptive Neuro-Fuzzy Inference System (ANFIS) is a robust method in solving non-linear classification by employing a human-readable interpretation manner. This paper verified a hybrid model, named WANFIS, where Whale Optimization Algorithm (WOA) was used for feature selection and tuning parameters of the ANFIS for land-cover classification. Hanoi,...

Prediction of estuary variation plays a crucial role for its unique brackish ecosystem and its importance in economic development. Yet, it is still a challenge to assess the estuary variation because it is influenced by multiple factors. Recently, with increasing availability of observation data, machine learning techniques have been increasingly u...

The authors wish to make the following corrections to this paper [...]

Meta-heuristic algorithms become common approaches in finding sufficiently good solutions for optimization problems. This study proposed and compared three novel hybrid methods, namely Biogeography-based Optimization (BBO), Gravitational Search Algorithm (GSA) and Grey Wolf Optimization (GWO) in combination with the popular Neural Network classifie...

In this paper, we propose a new scheme to analyze factors that affect outbreak of malaria using the Locally-Compensated Ridge Geographically Weighted Regression (LCR-GWR). Since malaria prevalence is location dependence, the relationships between natural and social-economic factors to the development and concentration of malaria hotspots have been...

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province...

This study examines the potentials of remotely sensed data, GIS and some machine learning classifiers and ensemble techniques in the investigation of the non-linear relationship between malaria occurrences and socio-physical conditions in the Dak Nong province of Viet Nam. Accuracy assessment was determined with Receiver Operating Characteristic (R...

The expansion of perennial crops area plays an important role for supporting the human livelihood in the Central Highlands, so have negative impacts on deforestation and sustainable development. Remote sensing and GIS were used to analyze the trajectories of perennial crops cover change in relationship with deforestation. The Logistic regression mo...

Perennial crops deliver strong economic, social and ecological benefits to many tropical countries. Accurate maps acquired through the remote sensing of the perennial crops are not yet available in the Central Highlands. The main objective of this study is to improve classification accuracy when mapping perennial crops in Bảo Lâm district with a hy...

This chapter presents a hybrid intelligent model for time series modeling and forecasting horizontal displacement of hydropower dams, named as ABC-LSSVR. In the proposed hybrid approach, least squares support vector regression (LSSVR) was used to create the displacement model. Furthermore, the model was optimized using the Artificial Bee Colony (AB...

House pricing is considered to be a complex social-economic process that is difficult to model with relevant accuracy. Based on Status Quality Trade Off theory, this paper aims to employ regression models, namely Artificial Neural Network (ANN) and Support vector machine (SVM) and Ensemble techniques, in estimating the sale prices of residential pr...

This paper proposes and validates a novel hybrid artificial intelligent approach, named as Particle Swarm Optimized Neural Fuzzy (PSO-NF), for spatial modeling of tropical forest fire susceptibility. In the proposed approach, a Neural Fuzzy inference system (NF) was used to establish the forest fire model whereas Particle Swarm Optimization (PSO) w...

Introduction There is a great concern on how to build up an interoperable health information system of public health and health information technology within the development of public information and health surveillance programme. Technically, some major issues remain regarding to health data visualization, spatial processing of health data, health...

Sediment depositions in estuaries directly impact the ecosystem and local communities whose livelihoods depend on aquaculture and fishing. It is difficult to predict changes of the inlet in short term, due to complex processes of sedimentation. In this paper, we proposed an approach to identify the dominant factors (or elements) driving the morphol...

Sediment depositions in estuaries directly impact the ecosystem and local communities whose livelihoods depend on aquaculture and fishing. It is difficult to predict changes of the inlet in short term, due to complex processes of sedimentation. In this paper, we proposed an approach to identify the dominant factors (or elements) driving the morphol...

This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algo...

The article reports on a project to align the provision of education with Vietnam's growing information requirements in order ensure a strong foundation for land administration in the future. The rate of urbanization in Vietnam is relatively high: in the Southeast, due to the presence of Ho Chi Minh City, the urban proportion of the population incr...

Despite the unprecedented rate of urbanization around the world, information regarding land use planning and management is not updated frequently enough to accurately track this urban change. In order to monitor changes in the urban environment, an understanding of the change in patterns of urban development over time is becoming increasingly impor...