Construction and deduction of rainstorm disaster scenarios based on Bayesian networks

JIANG Bo; ZHANG Chao; CHEN Tao; YUAN Hongyong; FAN Weicheng

doi:10.16511/j.cnki.qhdxxb.2020.22.039

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Journal of Tsinghua University(Science and Technology) ›› 2021, Vol. 61 ›› Issue (6) : 509-517. DOI: 10.16511/j.cnki.qhdxxb.2020.22.039

DISASTER PREVENTION AND MITIGATION

Construction and deduction of rainstorm disaster scenarios based on Bayesian networks

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Abstract

Rainstorm disasters are characterized by high intensity and long duration that lead to secondary events and difficult emergency response. This study analyzed rainstorm risks from a global perspective. An emergency scenario was constructed that considered the complexity of the secondary events to analyze the entire development of a rainstorm scenario. A Bayesian network (BN) was used with various risk factor probabilities to analyze the flood risks caused by various rainstorms. The key nodes in the network were then identified by a sensitivity analysis of the risk factors. The results show that the rainstorm risk analysis based on this BN model can help decision makers evaluate storm conditions, develop response plans, identify key risk factors, and improve the timeliness and effectiveness of emergency responses.

Key words

rainstorm / Bayesian network / scenario construction / risk factor

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JIANG Bo, ZHANG Chao, CHEN Tao, YUAN Hongyong, FAN Weicheng. Construction and deduction of rainstorm disaster scenarios based on Bayesian networks[J]. Journal of Tsinghua University(Science and Technology). 2021, 61(6): 509-517 https://doi.org/10.16511/j.cnki.qhdxxb.2020.22.039

References

[1] 苏伯尼, 黄弘, 张楠. 基于情景模拟的城市内涝动态风险评估方法[J]. 清华大学学报(自然科学版), 2015, 55(6):684-690. SU B N, HUANG H, ZHANG N. Dynamic urban waterlogging risk assessment method based on scenario simulations[J]. Journal of Tsinghua University (Science and Technology), 2015, 55(6):684-690. (in Chinese)
[2] 丁继勇, 王卓甫, 郭光祥. 基于贝叶斯和动态博弈分析的城市暴雨内涝应急决策[J]. 统计与决策, 2012(23):26-29. DING J Y, WANG Z F, GUO G X. Emergency decision-making of urban rainstorm and waterlogging based on Bayesian and dynamic game analysis[J]. Statistics and Decision, 2012(23):26-29. (in Chinese)
[3] 孙超, 钟少波, 邓羽. 基于暴雨内涝灾害情景推演的北京市应急救援方案评估与决策优化[J]. 地理学报, 2017, 72(5):804-816. SUN C, ZHONG S B, DENG Y. Scenario deduction based emergency rescue plan assessment and decision optimization of urban rainstorm water-logging:A case study of Beijing[J]. Acta Geographica Sinica, 2017, 72(5):804-816. (in Chinese)
[4] 赵庆良, 王军, 许世远. 基于情景的沿海城市社区暴雨洪水风险评价:以温州龙湾区为例[C]//中国视角的风险分析和危机反应(RAC-2010). 长春, 中国, 2010:504-512. ZHAO Q L, WANG J, XU S Y. Flood risk assessment of coastal community based on scenario:A case study in Longwan district of Wenzhou City[C]//Chinese Perspective on Risk Analysis and Crisis Response (RAC-2010). Changchun, China, 2010:504-512. (in Chinese)
[5] 张振国, 温家洪. 基于情景模拟的城市社区暴雨内涝灾害危险性评价[J]. 中国人口资源与环境, 2014, 24(5):478-482. ZHANG Z G, WEN J H. Hazard assessment of rainstorm waterlogging in urban communities based on scenario simulation[J]. China Population, Resources and Environment, 2014, 24(5):478-482. (in Chinese)
[6] 范维澄, 刘奕, 翁文国. 公共安全科技的"三角形"框架与"4+1"方法学[J]. 科技导报, 2009, 27(6):3. FANG W C, LIU Y, WENG W G. Triangular framework and "4+1" methodology for public security science and technology[J]. Science & Technology Review, 2009, 27(6):3. (in Chinese)
[7] 史培军. 再论灾害研究的理论与实践[J]. 自然灾害学报, 1996, 5(4):6-17. SHI P J. Theory and practice of disaster study[J]. Journal of Natural Disasters, 1996, 5(4):6-17. (in Chinese)
[8] 范海军, 肖盛燮, 郝艳广, 等. 自然灾害链式效应结构关系及其复杂性规律研究[J]. 岩石力学与工程学报, 2006, 25(S1):2603-2611. FAN H J, XIAO S X, HAO Y G, et al. Study on structural relation of chain effects on natural disaster and its complexity[J]. Chinese Journal of Rock Mechanics and Engineering, 2006, 25(S1):2603-2611. (in Chinese)
[9] 裘江南, 师花艳, 叶鑫, 等. 基于事件的定性知识表示模型[J]. 系统工程, 2009, 27(10):1-8. QIU J N, SHI H Y, YE X, et al. Qualitative knowledge representing model based on events[J]. Systems Engineering, 2009, 27(10):1-8. (in Chinese)
[10] PEARL J. Probabilistic reasoning in intelligent systems:Networks of plausible inference[M]. San Francisco, USA:Morgan Kaufmann Publishers, 1988.
[11] 王军, 周伟达. 贝叶斯网络的研究与进展[J]. 电子科技, 1999(15):6-7. WANG J, ZHOU W D. Research and development of Bayesian networks[J]. Electronic Science and Technology, 1999(15):6-7. (in Chinese)
[12] KIM S, KIM Y E, BAE K J, et al. NEST:A quantitative model for detecting emerging trends using a global monitoring expert network and Bayesian network[J]. Futures, 2013, 52:59-73.
[13] GONG Y S, WANG Y Z. Application research on Bayesian network and D-S evidence theory in motor fault diagnosis[C]//2013 6th International Conference on Intelligent Networks and Intelligent Systems. Shenyang, China, 2013.
[14] ZHANG C, WU J S, HUANG C, et al. A model for the representation of emergency cases[J/OL]. Natural Hazards, 2018, 91:337-351. DOI:10.1007/s11069-017-3131-9.