Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network
Drivers of urban flood disaster risk may be related to many factors from nature and society. However, it is unclear how these factors affect each other and how they ultimately affect the risk. From the perspective of risk uncertainty, flood inundation risk is considered to be the probability of inun...
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| Format: | Article |
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MDPI AG
2021-02-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/10/2/210 |
| _version_ | 1797395905883668480 |
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| author | Shanqing Huang Huimin Wang Yejun Xu Jingwen She Jing Huang |
| author_facet | Shanqing Huang Huimin Wang Yejun Xu Jingwen She Jing Huang |
| author_sort | Shanqing Huang |
| collection | DOAJ |
| description | Drivers of urban flood disaster risk may be related to many factors from nature and society. However, it is unclear how these factors affect each other and how they ultimately affect the risk. From the perspective of risk uncertainty, flood inundation risk is considered to be the probability of inundation consequences under the influence of various factors. In this paper, urban flood inundation risk assessment model is established based on Bayesian network, and then key disaster-causing factors chains are explored through influence strength analysis. Jingdezhen City is selected as study area, where the flood inundation probability is calculated, and the paths of these influential factors are found. The results show that the probability of inundation in most areas is low. Risk greater than 0.8 account for about 9%, and most of these areas are located in the middle and southern section of the city. The influencing factors interact with each other in the form of factor chain and, finally, affect the flood inundation. Rainfall directly affects inundation, while river is the key factor on inundation which is influenced by elevation and slope. In addition, in the chain of socio-economic factors, the population will determine the pipe density through affecting gross domestic product (GDP), and lead to the inundation. The approach proposed in this study can be used to find key disaster-causing factors chains, which not only quantitatively reveal the formation of risks but also provide reference for early warning. |
| first_indexed | 2024-03-09T00:42:47Z |
| format | Article |
| id | doaj.art-4c10cffa22d04bd6a4c79180f3fa6412 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-09T00:42:47Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
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| series | Land |
| spelling | doaj.art-4c10cffa22d04bd6a4c79180f3fa64122023-12-11T17:43:27ZengMDPI AGLand2073-445X2021-02-0110221010.3390/land10020210Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian NetworkShanqing Huang0Huimin Wang1Yejun Xu2Jingwen She3Jing Huang4Institute of Management Science, Business School, Hohai University, Nanjing 211100, ChinaInstitute of Management Science, Business School, Hohai University, Nanjing 211100, ChinaInstitute of Management Science, Business School, Hohai University, Nanjing 211100, ChinaInstitute of Management Science, Business School, Hohai University, Nanjing 211100, ChinaInstitute of Management Science, Business School, Hohai University, Nanjing 211100, ChinaDrivers of urban flood disaster risk may be related to many factors from nature and society. However, it is unclear how these factors affect each other and how they ultimately affect the risk. From the perspective of risk uncertainty, flood inundation risk is considered to be the probability of inundation consequences under the influence of various factors. In this paper, urban flood inundation risk assessment model is established based on Bayesian network, and then key disaster-causing factors chains are explored through influence strength analysis. Jingdezhen City is selected as study area, where the flood inundation probability is calculated, and the paths of these influential factors are found. The results show that the probability of inundation in most areas is low. Risk greater than 0.8 account for about 9%, and most of these areas are located in the middle and southern section of the city. The influencing factors interact with each other in the form of factor chain and, finally, affect the flood inundation. Rainfall directly affects inundation, while river is the key factor on inundation which is influenced by elevation and slope. In addition, in the chain of socio-economic factors, the population will determine the pipe density through affecting gross domestic product (GDP), and lead to the inundation. The approach proposed in this study can be used to find key disaster-causing factors chains, which not only quantitatively reveal the formation of risks but also provide reference for early warning.https://www.mdpi.com/2073-445X/10/2/210urban flood inundationfactors chainsrisk assessmentBayesian networkinfluence strengthsensitivity analysis |
| spellingShingle | Shanqing Huang Huimin Wang Yejun Xu Jingwen She Jing Huang Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network Land urban flood inundation factors chains risk assessment Bayesian network influence strength sensitivity analysis |
| title | Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network |
| title_full | Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network |
| title_fullStr | Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network |
| title_full_unstemmed | Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network |
| title_short | Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network |
| title_sort | key disaster causing factors chains on urban flood risk based on bayesian network |
| topic | urban flood inundation factors chains risk assessment Bayesian network influence strength sensitivity analysis |
| url | https://www.mdpi.com/2073-445X/10/2/210 |
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