Bayesian Network-based Inferential Analysis of Xigou Reservoir Overtopping Accident

Authors

  • Yilei Wei

DOI:

https://doi.org/10.54097/trj2q784

Keywords:

Bayesian Network, Scenario Analysis, Reservoir Dam-Break, Scenario Deduction

Abstract

With the development of society and the increasing tension of water resources, the importance of reservoir management has become increasingly prominent. In March 2021, a significant overtop-ping accident occurred at the Xigou Dam, an auxiliary project of the Xiaolangdi Water Conservancy Project (also known as the Xigou Reservoir). The direct cause of the accident was an electrical failure, but it also involved subjective factors such as low risk awareness and weak daily management. This paper extracts 17 scenario nodes from the "Xigou Reservoir Overtopping" accident through scenario analysis and constructs a scenario Bayesian network model by analyzing the progression of the accident. The conditional probability of a node is calculated using the expert scoring method to compute the state probability of the node. Finally, the probabilistic changes in the states of ‘water level’, ‘casualties’ and ‘economic losses’ are analysed by adjusting the prior probabilities of the selected nodes. In the development of this accident, the conditions of equipment maintenance and the implementation of emergency response activities have a significant im-pact on the accident progression. Under conditions of normal equipment maintenance or active emergency response, the probability of the 'water level status' being normal increases by over 20%. Additionally, under normal staff supervision, the probability of the 'water level status' being normal also increases by 14%. The analysis of projected accidents highlights the substantial impact of human factors on these incidents. Therefore, it is crucial to establish robust personnel supervision mechanisms and implement effective equipment maintenance plans to prevent and mitigate risks.

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References

[1] Li Hongen, Wang Fang, Zhao Jianguo. Study on the risk evolution mechanism of dam failure combined with historical dam failure statistics. China Water Resources. 2024(08):40-45.

[2] Sheng Jinbao, Li Hongen, Sheng Taozhen. Statistical analysis of dam failure and its loss of life in China. Hydro-Science and Engineering, 2023(1): 1-15.

[3] Xu Yao, Zhao Chun, Wang Yang. Research on Comprehensive Indicators for Risk Ranking of Reservoir Dams Based on Principal Component Analysis Method. Water Resources Development Research, 2018, 18(02):43-47. DOI:10.13928 /j.cnki. wrdr. 2018.02.012.

[4] Ding Wei, Jin Youjie, Zhang Ri, et al. Evaluation and prediction of potential risks of reservoir dam infrastructures based on XGBoost. Yangtze River, 2023, 54(04):241-246. DOI: 10.16232/j.cnki.1001-4179.2023.04.035.

[5] Tang Xianqi, Shi Yuqun, Yang Haiyun, et al. Dam risk assessment based on Bayesian network inference. Engineering Journal of Wuhan University, 2024, 57(02):152-158. DOI:10. 14188/ j.1671-8844.2024-02-003.

[6] Lin Pengyuan, Li Hongen, Xu Kang, et al. Bayesian network-based analysis on seepage risk of a reservoir dam. Water Resources and Hydropower Engineering, 2022, 53(11):110-120. DOI: 10.13928/j.cnki.wrahe.2022.11.011.

[7] Pan Bin. Functional demand analysis of urban rail transit emergency command system based on scenario analysis. Transport Business China, 2021(36):7-9. DOI: 10.3969/j. issn. 1673-3681. 2021.36.003.

[8] Ren Yongcun, Zhang Ren, Zhang Yongsheng, et al. Scenario analysis and simulation deduction of the "Zhengzhou Rainstorm Subway Disaster Event" based on Bayesian network. Transactions of Atmospheric Sciences, 2023, 46(06):904-916.

[9] Ge Wei, Jiao Yutie, Hong Xinqian, et al. Risk Assessment of Life Loss Caused by Dam Breach Based on AHP-BN Method. Journal of Zhengzhou University (Engineering Science), 2021, 42 (03):8-12.

[10] Zhang Jianyun, Sheng Jinbao, Jin Junliang, Zhang Shichen, et al. The Problems and Countermeasures of Reservoir-dam Emergency Management in China. Journal of China Emergency Management Science, 2022(09):23-30.

[11] Lin Pengzhi, Chen Yu. Risk Analysis of Dam Overtopping for Cascade Reservoirs Based on Bayesian Network. Advanced Engineering Sciences, 2018,50(03):46-53. DOI: 10.15961/j. jsuese. 201800332.

[12] Wu Yifan. Study on Dam Overtopping Risk in Karst Areas under Different Use Scenarios. Guangxi University, 2023. DOI: 10. 27034/d.cnki.ggxiu.2022.000811.

[13] Li, Wei et al. “Environmental impact evaluation model of dam breach —considering the uncertainty feature of environment.” Desalination and Water Treatment 183 (2020): 131-138.

[14] Hexiang Z, Wei G, Yadong Z, et al. Risk Management Decision of Reservoir Dams Based on the Improved Life Quality Index. Water Resources Management, 2023,37(3): 1223-1239. DOI: 10.1007/S11269-023-03426-Y.

[15] Sun Linfang, Xu Hui, Li Jinhai, et al. Classification Model of New Media Events Based on Bayesian Network. Computer and Modernization. 2014,05:65-69+73.

[16] He Zhaoze, Mo Junwen. Housing Waterproof Risk Based on Bayesian Network. Journal of Engineering Management, 2015, 01:86-90.

[17] Wang Shaoying. Design of a Risk-benefit Assessment System for Unmanned Farm Investments based on BN-DT. Agricultural Machinery Using & Maintenance. 2023(12):15-20. DOI: 10.14031/j.cnki.njwx.2023.12.004.

[18] Guo, Liang, Y. Zhao, and F. Y. Cui. "A new fault diagnosis method based on Bayesian network model in a wastewater treatment plant of northern China." Desalination and water treatment (2016):1-10.

[19] Weijing Niu. Research on Key Issues and Optimization Strategies for Emergency Response to Public Health Emergencies. Applied Mathematics and Nonlinear Sciences, 2024, 9 (1).

[20] Wu Jiansong, Xu Shengdi, Zhou Rui, et al. Scenario analysis of mine water inrush hazard using Bayesian networks. Safety Science, 2016, 89: 231-239.

[21] Xin Peiwei, FAISAL K, SALIM A. Dynamic hazarded entification and scenario mapping using Bayesian network. Process Safety and Environmental Protection, 2017, 105: 143-155.

[22] Xie Xiaoliang, Huang Linglu, Marson Stephen M., Wei Guo. Emergency response process for sudden rainstorm and flooding: scenario deduction and Bayesian network analysis using evidence theory and knowledge meta-theory. Natural Hazards, 2023, 117 (3).

[23] Yuan Xiaofang, Tian Shuicheng, et al. Scenario Analysis of Unconventional Emergency Based on PSR Model and Bayesian Networks. China Safety Science Journal, 2011,01: 169-176.

[24] Xiaoliang X, Linglu H, M. S M, et al. Emergency response process for sudden rainstorm and flooding: scenario deduction and Bayesian network analysis using evidence theory and knowledge meta-theory. Natural Hazards, 2023, 117 (3): 3307-3329.

[25] Lan Zequan, Li Yulin, et al. Scenario deduction of gas explosion accidents in coal mine fire areas based on Bayesian network. Journal of North China Institute of Science and Technology, 2023,20(06):16-22. DOI: 10.19956/j.cnki.ncist. 2023.06.003.

[26] Jiang Yunzhong, Zhang Rui, Wang Bende. Scenario-based approach for emergency operational response: Implications for reservoir management decisions. International Journal of Disaster Risk Reduction, 2022, 80.

[27] Turkel O A, Zaifoglu H, Yanmaz M A. Probabilistic modeling of dam failure scenarios: a case study of Kanlikoy Dam in Cyprus. Natural Hazards, 2024, 120 (11): 10087-10117.

[28] A'kif A, Nouh A M, Saad A, et al. Hydrological and Hydrodynamic Modeling for Flash Flood and Embankment Dam Break Scenario: Hazard Mapping of Extreme Storm Events. Sustainability, 2023, 15 (3): 1758-1758.

[29] Yuan C, Ma S, Hu Y, et al. Scenario Deduction on Fire Accidents for Oil–Gas Storage and Transportation Based on Case Statistics and a Dynamic Bayesian Network. Journal of Hazardous, Toxic, and Radioactive Waste, 2020, 24(3).

[30] Li S, Chen S, Liu Y. A Method of Emergent Event Evolution Reasoning Based on Ontology Cluster and Bayesian Network. IEEE Access, 2019, 7:15230-15238.

[31] Fan C, An R, Li J, et al. An Approach Based on the Protected Object for Dam-Break Flood Risk Management Exemplified at the Zipingpu Reservoir. International Journal of Environmental Research and Public Health,2019,16(19):3786-3786.

[32] She J, Guo Z, Li Z, et al. Research on scenario deduction and emergency decision-making evaluation for construction safety accidents. Reliability Engineering and System Safety,2024, 251110317-110317.

[33] Xiaoliang X,Yuzhang T ,Guo W .Deduction of sudden rainstorm scenarios: integrating decision makers' emotions, dynamic Bayesian network and DS evidence theory. Natural Hazards,2022,116(3):2935-2955.

[34] Yuan C, Ma S, Hu Y, et al. Scenario Deduction on Fire Accidents for Oil­–Gas Storage and Transportation Based on Case Statistics and a Dynamic Bayesian Network. Journal of Hazardous, Toxic, and Radioactive Waste (2020),24(3): 040 20004-04020004.

[35] Glotov E V, Chlachula J, Glotova P L, et al. Causes and environmental impact of the gold-tailings dam failure at Karamken, the Russian Far East. Engineering Geology (2018), 245236-247.

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Published

29-12-2024

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Articles

How to Cite

Wei, Y. (2024). Bayesian Network-based Inferential Analysis of Xigou Reservoir Overtopping Accident. Frontiers in Computing and Intelligent Systems, 10(3), 71-78. https://doi.org/10.54097/trj2q784