Mei Cai, Yu Gao, Chen Yang, Jin-bang Xiao, Qiuhan Wang
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After sensitivity analysis, we conclude that the ‘catastrophic index of tropical cyclones,’ ‘population density,’ and ‘prevention capacity’ have important impacts on social vulnerability. Human traits, the social environment, and the economy play important roles in social vulnerability assessments. Therefore, reducing the catastrophic index of tropical cyclones and population density and strengthening prevention capacity management measures are necessary. Some suggestions obtained after sensitivity analysis can assist governments in improving disaster prevention and mitigation abilities and formulating urban planning policies for sustainable development. Highlights Industry city’s vulnerability analysis for Natural-technological (Natech) accidents A social vulnerability assessment framework for sustainable development A Bayesian network combined with the Monte Carlo simulation data and expert judgment Risk management for high-consequence and low-probability events","PeriodicalId":50689,"journal":{"name":"Civil Engineering and Environmental Systems","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Social vulnerability assessment for an industrial city in Natech accidents: A Bayesian network approach\",\"authors\":\"Mei Cai, Yu Gao, Chen Yang, Jin-bang Xiao, Qiuhan Wang\",\"doi\":\"10.1080/10286608.2023.2211516\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT China is a large industrial country where tropical meteorological disasters occur frequently. Therefore, natural-technological (Natech) risk cannot be ignored. Assessing the social vulnerability of an industrial city prone to tropical meteorological disaster-induced Natechs is urgent. To analyze the social vulnerability of such cities, we propose a Bayesian network (BN)-based method to model the social vulnerability framework. Natech is characterised by high-consequence and low-probability. The industrial cities in Southeast China are selected as a case study. The Monte Carlo method simulates the data generated in industrial cities suffering from tropical disaster-induced Natechs, and the conditional probability tables of BN descendant nodes are obtained by the expert scoring method. After sensitivity analysis, we conclude that the ‘catastrophic index of tropical cyclones,’ ‘population density,’ and ‘prevention capacity’ have important impacts on social vulnerability. 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Highlights Industry city’s vulnerability analysis for Natural-technological (Natech) accidents A social vulnerability assessment framework for sustainable development A Bayesian network combined with the Monte Carlo simulation data and expert judgment Risk management for high-consequence and low-probability events\",\"PeriodicalId\":50689,\"journal\":{\"name\":\"Civil Engineering and Environmental Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2023-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Civil Engineering and Environmental Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/10286608.2023.2211516\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Civil Engineering and Environmental Systems","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10286608.2023.2211516","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Social vulnerability assessment for an industrial city in Natech accidents: A Bayesian network approach
ABSTRACT China is a large industrial country where tropical meteorological disasters occur frequently. Therefore, natural-technological (Natech) risk cannot be ignored. Assessing the social vulnerability of an industrial city prone to tropical meteorological disaster-induced Natechs is urgent. To analyze the social vulnerability of such cities, we propose a Bayesian network (BN)-based method to model the social vulnerability framework. Natech is characterised by high-consequence and low-probability. The industrial cities in Southeast China are selected as a case study. The Monte Carlo method simulates the data generated in industrial cities suffering from tropical disaster-induced Natechs, and the conditional probability tables of BN descendant nodes are obtained by the expert scoring method. After sensitivity analysis, we conclude that the ‘catastrophic index of tropical cyclones,’ ‘population density,’ and ‘prevention capacity’ have important impacts on social vulnerability. Human traits, the social environment, and the economy play important roles in social vulnerability assessments. Therefore, reducing the catastrophic index of tropical cyclones and population density and strengthening prevention capacity management measures are necessary. Some suggestions obtained after sensitivity analysis can assist governments in improving disaster prevention and mitigation abilities and formulating urban planning policies for sustainable development. Highlights Industry city’s vulnerability analysis for Natural-technological (Natech) accidents A social vulnerability assessment framework for sustainable development A Bayesian network combined with the Monte Carlo simulation data and expert judgment Risk management for high-consequence and low-probability events
期刊介绍:
Civil Engineering and Environmental Systems is devoted to the advancement of systems thinking and systems techniques throughout systems engineering, environmental engineering decision-making, and engineering management. We do this by publishing the practical applications and developments of "hard" and "soft" systems techniques and thinking.
Submissions that allow for better analysis of civil engineering and environmental systems might look at:
-Civil Engineering optimization
-Risk assessment in engineering
-Civil engineering decision analysis
-System identification in engineering
-Civil engineering numerical simulation
-Uncertainty modelling in engineering
-Qualitative modelling of complex engineering systems