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Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network

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  • Shanqing Huang

    (Institute of Management Science, Business School, Hohai University, Nanjing 211100, China
    State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering, Nanjing 210098, China)

  • Huimin Wang

    (Institute of Management Science, Business School, Hohai University, Nanjing 211100, China
    State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering, Nanjing 210098, China)

  • Yejun Xu

    (Institute of Management Science, Business School, Hohai University, Nanjing 211100, China)

  • Jingwen She

    (Institute of Management Science, Business School, Hohai University, Nanjing 211100, China)

  • Jing Huang

    (Institute of Management Science, Business School, Hohai University, Nanjing 211100, China)

Abstract

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.

Suggested Citation

  • Shanqing Huang & Huimin Wang & Yejun Xu & Jingwen She & Jing Huang, 2021. "Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network," Land, MDPI, vol. 10(2), pages 1-21, February.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:2:p:210-:d:502389
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    References listed on IDEAS

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    1. Zening Wu & Yanxia Shen & Huiliang Wang, 2019. "Assessing Urban Areas’ Vulnerability to Flood Disaster Based on Text Data: A Case Study in Zhengzhou City," Sustainability, MDPI, vol. 11(17), pages 1-15, August.
    2. Rui Liu & Yun Chen & Jianping Wu & Lei Gao & Damian Barrett & Tingbao Xu & Xiaojuan Li & Linyi Li & Chang Huang & Jia Yu, 2017. "Integrating Entropy‐Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard," Risk Analysis, John Wiley & Sons, vol. 37(4), pages 756-773, April.
    3. Hongjun Joo & Changhyun Choi & Jungwook Kim & Deokhwan Kim & Soojun Kim & Hung Soo Kim, 2019. "A Bayesian Network-Based Integrated for Flood Risk Assessment (InFRA)," Sustainability, MDPI, vol. 11(13), pages 1-15, July.
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    1. Jozef Kubás & Katarína Bugánová & Mária Polorecká & Katarína Petrlová & Adéla Stolínová, 2022. "Citizens’ Preparedness to Deal with Emergencies as an Important Component of Civil Protection," IJERPH, MDPI, vol. 19(2), pages 1-18, January.
    2. Phichet Munpa & Atima Dubsok & Athit Phetrak & Wandee Sirichokchatchawan & Nutta Taneepanichskul & Jenyuk Lohwacharin & Suthirat Kittipongvises & Chongrak Polprasert, 2024. "Building a Resilient City through Sustainable Flood Risk Management: The Flood-Prone Area of Phra Nakhon Sri Ayutthaya, Thailand," Sustainability, MDPI, vol. 16(15), pages 1-21, July.
    3. Mo Wang & Xiaoping Fu & Dongqing Zhang & Furong Chen & Jin Su & Shiqi Zhou & Jianjun Li & Yongming Zhong & Soon Keat Tan, 2023. "Urban Flooding Risk Assessment in the Rural-Urban Fringe Based on a Bayesian Classifier," Sustainability, MDPI, vol. 15(7), pages 1-16, March.
    4. Chao Ma & Wenchao Qi & Hongshi Xu & Kai Zhao, 2022. "An integrated quantitative framework to assess the impacts of disaster-inducing factors on causing urban flood," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(3), pages 1903-1924, September.

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