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韧性城市视角下地铁洪涝灾害风险分析——基于Bow-Tie—贝叶斯网络模型 被引量:16

Research on Subway Flood Disaster from the Perspective of Resilient City——Based on Bow-tie—Bayesian Network Model
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摘要 极端暴雨易引发地铁洪涝灾害,其致灾因子具有复杂性特征,辨识地铁洪涝灾害的致灾因素,可以有效提升地铁系统应对洪涝灾害的韧性。为此,建立了Bow-Tie—贝叶斯网络的灾害风险分析模型,利用事故树构建致灾模型,再以贝叶斯网络模型预测灾害的后验概率。通过节点后验概率、关键重要度分析等对地铁洪涝灾害的基本事件的重要度进行分析,提取出导致灾害发生的关键基本事件。分析结果表明,地铁和环境的不安全状态和管理的缺陷是导致灾害发生的重要中间事件。该文从灾前预防、灾中抵御、灾后恢复三个维度对地铁洪涝灾害提出针对性的对策建议,对建设更具韧性和包容性的社会-生态系统具有重要的借鉴意义。 Extreme rainstorm is easy to cause subway flood disaster,and its disaster causing factors have complex characteristics.Identifying the disaster causing factors of subway flood disaster can effectively improve the resilience of subway system.Therefore,this paper establishes the disaster risk analysis model of Bow-tie—Bayesian network,constructs the disaster causing model by using the Fault tree,and then uses the Bayesian network model to predict the posterior probability of disaster.Through the analysis of node posterior probability and critical importance,the importance of the basic events of subway flood disaster is analyzed,and the key basic events leading to the disaster are extracted.The analysis results show that the unsafe state and management defects of subway and environment are important intermediate events leading to disasters.This paper puts forward targeted countermeasures and suggestions for subway flood disaster from the three dimensions of pre disaster prevention,disaster resistance and post disaster recovery,which has important reference significance for building a more resilient and inclusive social ecosystem.
作者 闫绪娴 王俊丽 范玲 李文超 YAN Xuxian;WANG Junli;FAN Ling;LI Wenchao(School of Management Science & Engineering,Shanxi University of Finance and Economics,Taiyuan 030006,China;Department of Maths,Taiyuan Normal University,Jinzhong 030619,China)
出处 《灾害学》 CSCD 北大核心 2022年第2期36-43,共8页 Journal of Catastrophology
基金 国家社科学基金一般项目“我国城市暴雨内涝灾害形成机理、韧性评估与防治对策研究”(20BGL260)。
关键词 韧性城市 Bow-Tie—贝叶斯网络模型 地铁洪涝灾害 重要度分析 resilient city bow-tie—Bayesian network model subway flood disaster importance analysis
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