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The purpose of this research is to identify the indicators of typhoon damage and develop a metric for typhoon vulnerability functions employing the losses associated with Typhoon Maemi. Typhoons cause significant financial damages worldwide every year. Federal and local governments, insurance companies, and construction companies strive to develop...
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... Research on hazard factors centers on the intensity and frequency of typhoons [16]. The vulnerability assessment of typhoon disasters mainly includes the resistance and recovery ability of the region after a typhoon [17]. For example, some researchers used two typical typhoons to explore vulnerability changes, finding that rainfall intensity and wind intensity were the most critical factors [18]. ...
The evaluation of typhoon disaster risk is a widely discussed global topic. Currently, the index system method has become a common approach for the evaluation of typhoon disaster risk. However, the indices within the system are calculated independently, and subjective human factors significantly influence the assignment of index weights. The existing studies lack purely quantitative assessment methods, which makes the studies less precise and more difficult for other researchers to replicate. To bridge this gap, this study employs emergy analysis methods based on thermodynamics to develop a typhoon disaster risk evaluation index system for China’s coastal zone. Without the interference of weights and other human factors, the system contains various quantitative indices, including aggregate impelling energy, typhoon intensity emergy, adaptability emergy, the vulnerability index, and the integrated typhoon hazard index. Subsequently, these indices and socio-economic data were spatialized, and the evaluation of typhoon disaster risk was conducted at the city grid level in the coastal zone of China. The findings reveal that the high-risk areas for typhoon disasters in China are concentrated in prefecture-level cities along the southeast coast. The typhoon disaster risk index is higher in the southern region compared to the northern region, with a decreasing trend in the distribution of the integrated typhoon hazard index from coastal to inland areas. The aim of this study is to use a new quantitative evaluation method (emergy) to evaluate typhoon disasters. It also serves as a theoretical foundation and technical support for national and local governments in the formulation of policies for disaster prevention and reduction.
... Jeong and Cheong (2012) and Gao et al. (2020) laid some groundwork in this area; they primarily subsume typhoon vulnerability within a broader analysis of urban-coupled vulnerability, failing to fully account for the urban spatial vulnerability factors specific to typhoon disasters [28,32]. Kim et al. (2020) attempted to develop a typhoon vulnerability function using loss records from Typhoon Maemi. However, their research should have comprehensively considered the impact of urban spatial layout and environmental characteristics [33]. ...
... Kim et al. (2020) attempted to develop a typhoon vulnerability function using loss records from Typhoon Maemi. However, their research should have comprehensively considered the impact of urban spatial layout and environmental characteristics [33]. Similarly, Yan et al. (2023), in exploring the spatiotemporal variations in typhoon risk in Guangdong Province, needed to provide a specialised assessment indicator and framework for typhoon vulnerability [34]. ...
Typhoons are extremely severe weather events which seriously threaten the safety of people’s lives and properties. Therefore, identifying and controlling typhoon disaster hazards have become important research topics. The spatial–temporal characteristics of typhoons are analysed using the typhoon disaster data in Macau from 2000 to 2020. Computational fluid dynamics (CFD) numerical simulation is adopted to understand the 3D urban wind environment. Moreover, the ‘exposure, sensitivity and adaptation’ evaluation model is applied to construct the study framework. To calculate urban disaster vulnerability, the Create Fishnet tool is used to divide the city of Macau into 470 grids. The principal component analysis method is used to reveal the factors that significantly affect the typhoon’s vulnerable areas. Result shows that 31.27% of grids are severely vulnerable. In addition, six principal components are identified, including indicators such as population density, building area ratio, mean elevation and wind speed. This study verifies the feasibility of wind speed data obtained by CFD in the typhoon evaluation model. Moreover, it provides a reliable reference guide for future urban microlevel studies.
... The criticality of Busan to the Republic of Korea is juxtaposed with its exposure to extreme weather phenomena in the form of regular damaging typhoons that impact this region of the world annually. This was highlighted in September 2003 by the unprecedented devastation to the Korean peninsula caused by the super-typhoon "Maemi", which was concentrated in the Busan and Gyeongnam Provinces [3,4]. ...
... Across the Busan record, the super-typhoon "Maemi" event, which devastated gions of the South Korean Peninsula in September 2003, similarly overshadowed all o measured water levels at the tide gauge. The super-typhoon "Maemi" remains the m destructive event to make landfall on the Korean Peninsula since record keeping begu 1904, breaking a range of records for its size, intensity, and central pressure [3,4]. The height of the water level above the MSL during this event, at 1403 mm (12 tember 2003), was estimated to have a recurrence interval of around 98 years. ...
... Across the Busan record, the super-typhoon "Maemi" event, which devastated regions of the South Korean Peninsula in September 2003, similarly overshadowed all other measured water levels at the tide gauge. The super-typhoon "Maemi" remains the most destructive event to make landfall on the Korean Peninsula since record keeping begun in 1904, breaking a range of records for its size, intensity, and central pressure [3,4]. The height of the water level above the MSL during this event, at 1403 mm (12 September 2003), was estimated to have a recurrence interval of around 98 years. ...
This article conducts an extreme value analysis (EVA) of hourly tide gauge measurements at Busan, South Korea, from 1960 onwards to understand the influence of typhoon-driven surges and predicted tides that super-elevate ocean still water levels (SWLs) at Busan. The impact of the 2003 super-typhoon “Maemi” dominates the records, super-elevating the SWL above mean sea level (MSL) by 1403 mm, equating to a recurrence interval of 98 years, eclipsing the second highest measured extreme in August 1960, with a return level of around 16 years. The sensitivity testing of the random timing of high tides and typhoon storm surges reveals several near misses in recent history, where water levels attained at the Busan tide gauge could have surpassed the records set during the “Maemi” event. This paper explores the omnipresent increasing risk of continuously increasing sea level coupled with oceanic inundation associated with extreme phenomena. By integrating sea level projections (IPCC AR6), the result of the EVA provides important resources for coastal planning and engineering design purposes at Busan.
... Hence, active efforts of all mankind are required to prevent and limit this enormous damage [1]. Extreme weather events caused by climate change are occurring with greater frequency and severity than in the past and, therefore, can cause more serious damage to buildings, facilities and people [2,3]. Moreover, the 5th Evaluation Report of the Intergovernmental Panel on Climate Change (2014) warns against destructive impacts such as changes in average sea level, acidification, heavy rainfall and global increase in average temperature, and that these phenomena may accelerate [4]. ...
... In addition, to validate the final DNN model, a multiple regression analysis (MRA) model was developed using the same variables as applied in the DNN model. MRA is a widely used statistical method for prediction and quantification in both academic and industrial fields [3,18,48]. Subsequently, the MRA model was developed using the SPSS V23 software, and the model's MAE and RMSE values were calculated separately. Table 5 illustrates the comparative results between the proposed DNN model and the conventional MRA model. ...
Climate crises such as extreme weather events, natural disasters and climate change caused by climate transformations are causing much damage worldwide enough to be called a climate catastrophe. The private sector and the government across industries are making every effort to prevent and limit the increasing damage, but the results have yet to meet market demand. Therefore, this study proposes a method that uses a deep learning algorithm to predict the damage caused by typhoons. Model development is based on a Deep Neural Network (DNN) algorithm, and learning data is obtained by fine-tuning the network structure and hyperparameters; the amount of damage caused by Typhoon Rusa was known as training data. The constructed DNN model underwent evaluation and validation by computation of mean absolute error (MAE) and root mean square error (RMSE). Furthermore, a comparative analysis was conducted to confirm the applicability of the proposed framework against a traditional multi-regression model to ensure the model's accuracy and resilience. Finally, this study offers a novel approach to predicting typhoon damage using advanced deep-learning techniques. Subsequently, government disaster management officials, facility managers, and insurance companies can utilize this method to accurately predict the extent of damage caused by typhoons. Preventive actions such as improved risk assessment, expanded insurance companies, and enhanced disaster responses plans can be implemented using these outcomes. Ultimately, the proposed model will help to reduce typhoon damage and strengthen general resilience to climate crises.
... In 2019, Jiayang Zhang (Zhang and Chen, 2019) assessed the risk of flooding disaster caused by typhoon rainstorms, and their evaluation indexes included wind speed, rainfall, and elevation. Ji-Myong Kim et al. (2020) selected maximum wind speed and distance as evaluation indicators in the vulnerability analysis of typhoons in Korea. Therefore, in this study, DEM, total rainfall, maximum rainfall in a single day, distance from typhoon landfall, and atmospheric PM 2.5 concentration and NO 2 concentration were comprehensively selected as evaluation indicators. ...
Typhoon rain events are important factors that trigger changes in dissolved oxygen concentrations in watersheds. The direction of the typhoon driving force is clear, but the mode of action and mechanism are complex. Moreover, quantifying the relationship between these actions and dissolved oxygen is challenging. This study collected measured data from water quality monitoring and remote sensing during the 2022 typhoon rain events. By analyzing the changes in typhoon driving factors and dissolved oxygen (DO) concentrations in water under various typhoon storms, extended MOORA plus the full multiplicative form (MULTIMOORA), Multiscale Geographic Weighted Regression (MGWR), and spatial autocorrelation analysis were used to evaluate the response of DO concentration. Furthermore, the effects of the atmospheric environment under the influence of human activities on the response distribution of the urban water environment were analyzed. The results of the study showed that under the effect of a typhoon with higher rainfall intensity, the response of DO concentration in the water body of the river in the center of the city was better. However, the response of DO concentration in the water body at the mouth of the sea had a tendency to become worse. Under the influence of typhoon rain events with smaller intensity, the scouring effect of rainwater dominated, and the DO concentration response in the water body had a tendency to become worse. The analysis of spatial heterogeneity under the influence of human activities showed that the ranking values of DO concentration response in rivers in the city area of Zhongshan, under the influence of typhoon rain events, were positively correlated with the distribution of ozone (O3) concentration and sulfur dioxide (SO2) concentration in the eastern, central, and western parts of Zhongshan. Conversely, it was negatively correlated with the distribution of O3 concentration and SO2 concentration in the northern and southern parts of Zhongshan. Based on the research results, we constructed a technique to evaluate the response of dissolved oxygen concentration during the typhoon transit period, which can provide an indicator reference for urban managers in water environment management.
... Due to the lack of successive building damage observation data, assessing the physical vulnerability of buildings in an earthquake-debris flow disaster chain is a difficult task. Previous studies attempted to establish the joint building vulnerability curve of disaster chain based on the empirical vulnerability curves to single hazards including earthquakes (Calvi et al. 2006;Vicente et al. 2011;Suppasri et al. 2013;Karimzadeh et al. 2014) and debris flows (Fuchs et al. 2007;Papathoma-Koehle et al. 2012;Papathoma-Köhle et al. 2017;Kim et al. 2020;Luo, Fan, et al. 2020;Luo, Zhang, et al. 2020). The empirical building vulnerability indicates the cumulative probability of a specific building reaching or exceeding a certain degree of damage under a given hazard impact (Fell et al. 2005;Calvi et al. 2006;Foerster et al. 2009;Vamvatsikos et al. 2010;Quan Luna et al. 2011;). ...
Large earthquakes not only directly damage buildings but also trigger debris flows, which cause secondary damage to buildings, forming a more destructive earthquake-debris flow disaster chain. A quantitative assessment of building vulnerability is essential for damage assessment after a disaster and for pre-disaster prevention. Using mechanical analysis based on pushover, a physical vulnerability assessment model of buildings in the earthquake-debris flow disaster chain is proposed to assess the vulnerability of buildings in Beichuan County, China. Based on the specific sequence of events in the earthquake-debris flow disaster chain, the seismic vulnerability of buildings is 79%, the flow impact and burial vulnerabilities of damaged buildings to debris flow are 92% and 28% respectively, and the holistic vulnerability of buildings under the disaster chain is 57%. By comparing different vulnerability assessment methods, we observed that the physical vulnerability of buildings under the disaster chain process is not equal to the statistical summation of the vulnerabilities to independent hazards, which implies that the structural properties and vulnerability of buildings have changed during the disaster chain process. Our results provide an integrated explanation of building vulnerability, which is essential for understanding building vulnerability in earthquake-debris flow disaster chain and building vulnerability under other disaster chains.
... Extreme weather events called tropical cyclones, floods, heat waves, droughts, heavy rains, and cold waves have been a part of human history since the very beginning. However, the occurrence pattern of recent extreme weather events changes, the severity and frequency of it have augmented rapidly compared to the past, increasing the possibility of human casualties as well as the loss of buildings and facilities Kim et al., 2020a). The fifth Assessment Report (2014) of the Intergovernmental Panel on Climate Change warns of the negative effects of rising global mean air temperature, extreme rainfall, acidification and average sea level rise. ...
... The result of an analysis of the frequency and intensity of typhoons that invaded Korea from 1973 to 2019 showed that the severity of typhoons amplified and the risk of losses due to them enlarged (Kim et al., 2020a). Furthermore, instrumental data collected since 1904 indicates a rise in typhoon intensity on the Korean peninsula over the past century. ...
... As proved above, natural disasters worldwide cause tremendous property loss, which will increase with time. Two contributing factors are expected escalation of the severity and frequency of extreme weather events and surge in the value of citizens' property and in the development of cities (Kim et al., 2020a). In order to decrease the risk of financial loss due to natural disasters, the government and the private sector invest enormous budgets and time to establish prevention and recovery strategies. ...
The goal of this study is to suggest an approach to predict building loss due to typhoons using a deep learning algorithm. Due to the influence of climate change, the frequency and severity of typhoons gradually increase and cause exponential destruction of building. Therefore, related industries and the government are focusing their efforts on research and model development to quantify precisely the damage caused by typhoons. However, advancement in the accuracy of prediction is still needed, and the introduction of new technology, obtained due to the fourth revolution, is necessary. Therefore, this study proposed a framework for developing a model based on a deep neural network (DNN) algorithm for predicting losses to buildings caused by typhoons. The developed DNN model was tested and verified by calculating mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R ²). In addition, to further verify the robustness of the model, the applicability of the framework proposed in this study was verified through comparative verification with the conventional multi-regression model. The results and framework of this study will contribute to the present understanding by suggesting a deep learning method to predict the loss of buildings due to typhoons. It will also provide management strategies to related workers such as insurance companies and facility managers.
... It is expected that near-surface wind speed decreases when typhoon penetrates built-up areas in the wake of energy dissipation due to surface roughness elements. However, a number of studies bring to the attention that extreme winds which cause severe damage to buildings, trees, and civil facilities can still be observed in urban districts during the landfall of typhoons (Kleinen, 2007;Kim et al., 2020aKim et al., , 2020b. During the landfall of typhoon Mangkhut, up to seventeen thousand trees were blown down and hundreds of public facilities were impacted and suspended in Shenzhen. ...
Under the background of global climate change, typhoons have been attracting increasing attention due to their extraordinary destructive potential and great impact on the coastal areas of the South China Sea. Although the risk of strong winds related to typhoons has long been of interest, less is known about the underlying mechanism responsible for the severe near‐surface winds. Using eddy covariance data collected at four different heights on a 365‐m meteorological tower located in a coastal region, the characteristics of the convective typhoon boundary layer and the associated turbulence structures are compared with their counterparts in the “textbook” dynamically unstable boundary layer. In the convective typhoon boundary layer, bulk wind shear predominates the generation of mechanical turbulence, enhancing the vertical correlation between vertical layers. The spectral analysis highlights the salient features of turbulent structures under the convective typhoon boundary layer, confirming that the gust disturbance with the nondimensional frequency ranging from 0.003 to 0.3 modulates not only turbulent transports but also the horizontal flow. Such gusts with reduced phase difference enhance the downward momentum transport, mainly responsible for the maintenance of the strong near‐surface winds during typhoon landfalls.
... Guo et al. [35] coped with the vulnerability assessment issues for power transmission lines under typhoon weather on the basis of cascading failure state transition information diagram. Kim et al. [36] analyzed the typhoon vulnerability in South Korea through utilizing damage record model of typhoon Maemi. Ku et al. [37] solved the coastal vulnerability assessment issues of sea-level rise associated along with typhoon-induced surges in South Korea. ...
As one of the severe natural disasters, typhoon hazard brings tremendous tragedy to human beings. The foreland in the southeast of China is one of the most typhoon prone areas in the world. There are amount of damage of civil engineering structures induced by typhoon every year. Especially for the spacious villages, the low-rise buildings are vulnerable to typhoon so that many of them are destroyed regionally. The typhoon vulnerability assessment of civil engineering structures is a classical multiple attribute group decision making (MAGDM) issues. In this paper, the 2-tuple linguistic neutrosophic number grey relational analysis (2TLNN-GRA) method is built based on the grey relational analysis (GRA) and 2-tuple linguistic neutrosophic sets (2TLNSs) with incomplete weight information. For deriving the weight information of the attribute, an optimization model is built on the basis of the GRA, by which the attribute weights can be decided. Then, the optimal alternative is chosen through calculating largest relative relational degree from the 2-tuple linguistic neutrosophic number positive ideal solution (2TLNNPIS) which considers both the largest grey relational coefficient (GRC) from the 2TLNNPIS and the smallest GRC form 2-tuple linguistic neutrosophic number negative ideal solution (2TLNN NIS). Then, combine the traditional fuzzy GRA model with 2TLNNSs information, the 2TLNN-GRA method is established and the computing steps for MAGDM are built. Finally, a numerical example for typhoon vulnerability assessment of civil engineering structures has been given and some comparisons is used to illustrate advantages of 2TLNN-GRA method.
... This type of inspection is generalized and is not applicable to a specific roofing type. This survey is carried out by windstorm risk engineers who have expertise in evaluating and estimating wind vulnerabilities associated with an infrastructure system [5]. In the commercial property insurance industry, property risk surveys are often conducted following a request by property insurance underwriters, insured customers, risk managers, property brokers, Chief Financial Officers of a company, or an individual with an organization's risk & insurance responsibilities. ...
Insurance loss prevention surveys, specifically windstorm loss prevention surveys, involve the process of investigating the potential damage to a building or structure as the result of extreme weather conditions such as hurricanes or tornados. Traditionally, the loss prevention survey process is subjective, depending on the skills of the engineer conducting it. This study investigated the sensemaking process of risk engineers while conducting a loss prevention survey with special focus on the factors influencing it. The data frame theory of sensemaking was used as the framework for this study. A total of 15–20 h of interview data were collected from windstorm risk engineers, and the data were analyzed using an inductive thematic approach. The themes emerging from the data explained the sensemaking process of risk engineers, their process of making sense of contradictory information, the importance of the level of their experience, the internal and external biases influencing the inspection process, the difficulty in developing mental models and potential technology interventions. Recently, human in the loop systems such as drones have been used to improve the efficiency of windstorm loss prevention survey. The results from this study provide recommendations to guide the design of such systems to support the sensemaking process and situation awareness (SA) of the visual windstorm loss prevention survey.
