Scenario deduction of sudden mountain rainstorm disaster considering psychological factors of emergency subjects

doi:10.16265/j.cnki.issn1003-3033.2025.12.1739

Abstract

Abstract:

In order to effectively respond to sudden mountain rainstorm disaster and explore the impact of emergency subjects' psychology on disaster evolution, a method combining game theory and BN was used to simulate the disaster's progression. First, historical disaster cases were analyzed, and consultations with relevant experts were conducted to identify four key scenario elements: emergency scenarios, disaster-forming environment, emergency measures, and human psychology. The Jaccard index was then employed to determine the relationships between these scenarios, while game theory methods were applied to optimize the BN node probabilities, which were initially estimated through case analysis statistics and triangular fuzzy number methods. This led to the construction of a BN-based disaster simulation model that incorporated the psychological factors of emergency subjects in the context of sudden mountain rainstorm disaster. Finally, the model was applied to analyze the "8·21" mountain flood and debris flow disaster in Jinyang, Sichuan, and comparative experiments were carried out. The results demonstrate that the disaster reasoning model is feasible and superior, and the neglect of psychological factors leads to an underestimation of disaster risks. Therefore, emergency management agencies should consider the influence of human psychology on early warnings, evacuation, and resource allocation when designing emergency strategies, in order to improve the effectiveness and adaptability of their measures.

Key words: emergency subjects' psychology, sudden mountain rainstorm disaster, scenario deduction, Bayesian network (BN), game theory, case-analytical statistical method, triangular fuzzy number method

CLC Number:

X915.5

FANG Danhui, ZENG Niping, WANG Peipei. Scenario deduction of sudden mountain rainstorm disaster considering psychological factors of emergency subjects[J]. China Safety Science Journal, 2025, 35(12): 221-229.

Figures/Tables 10

Fig.1

Fig.2

Table 1

Components of sudden mountain rainstorm disaster situations

S	E	M	X
降雨开始S₁	极端天气、大气环流、台风来袭等E₁	发布气象预警,启动预案M₁	积极状态、消极状态X₁
强降雨持续,达到暴雨级别,做好灾害发生时的准备S₂	充足的水汽、强烈的气流上升运动和大气层结构的不稳定等E₂	提高应急响应水平,配备应急物资和设备,加固老旧房屋,清理出水口M₂	积极状态、消极状态X₂
地表积水,河道河水上涨,水库水位上涨S₃	—	大力疏通泄洪通道和排水系统,加强对山洪地质灾害易发点监测预警M₃	积极状态、消极状态X₃
积水及时排出,水位下降,洪水发生概率降低S₄	—	—	—
天气继续恶化,暴雨持续,人们进入备战状态S₅	大气环流稳定、地形降水效应仍显著、对流“列车效应”持续等E₃	强制可能受灾的单位停工停产,转移安置处于危险区的群众M₄	积极状态、消极状态X₄
人员及时避险,0伤亡S₆	—	—	—
河堤决口、溃坝、山洪/洪涝S₇	坡度陡峭、汇水地貌E₄	开挖泄流槽降低水位、加固河道堤防和水库坝体、24 h连续监测河流上游和水库的水位情况,根据监测信息及时排险M₅	积极状态、消极状态X₅
崩塌、滑坡、泥石流S₈	坡度陡峭、岩石风化、丰富的松散固体物源补给E₅	全面排查地质隐患、24 h连续监测山体位移变形情况,采取工程治理措施M₆	积极状态、消极状态X₆
人员伤亡严重、房屋倒塌、道路桥梁塌方、农业受损、断水断电断气事故S₉	—	启动应急响应,对危险区实行全面管制,疏通车辆,清理道路,搜救人员,危险区人员转移避险,抢修生命线系统,调拨救灾物资,疫情防控M₇	积极状态、消极状态X₇
人员全部撤离,无次生灾害发生,伤亡不再增加、危险被完全控制S₁₀	—	—	—
堰塞湖S₁₁	存在原有水系E₆	清理堆积物、爆破泄洪或开挖引流槽、泄流渠,水泵抽水M₈	积极状态、消极状态X₈
不发生溃决、暴雨消失 ${{S}_{}}_{12}$	—	—	—

Table 1

Fig.3

Fig.4

Table 2

Table 3

Fig.5

Fig.6

Fig.7

References 25

[1]	钟气. 建立气候风险早期预警系统, 科学应对极端天气气候事件[J]. 生命与灾害, 2024 (7): 14-17.
[2]	李哲, 夏中原, 兰光宇, 等. 汜水河洪涝风险模拟及灾害防控评价[J]. 自然灾害学报, 2024, 33 (3): 56-66.
	LI Zhe, XIA Zhongyuan, LAN Guangyu, et al. Flood risk simulation and disaster prevention and control evaluation of Sishui river[J]. Journal of Natural Disasters, 2024, 33(3): 56-66.
[3]	聚焦防汛抗旱\|强降雨导致广东省平远县38人死亡2人失联[EB/OL]. (2024-06-21). http://www.news.cn/local/20240621/322d5dc4c7a94edeb41c75ed6af4d50f/c.html.
[4]	中央广电总台央视新闻. 丹宁高速水阳段桥梁垮塌灾害调查评估报告公布 43名公职人员被问责[EB/OL]. (2025-03-27). https://news.cri.cn/20250327/a1a2611e-6525-aa01-4922-f21c2c7fba20.html.
[5]	多方力量紧急驰援贵州大方果瓦乡山体滑坡救援持续进行[EB/OL]. (2025-05-23). https://gz.cnr.cn/jiaodiantu/20250523/t20250523_527183121.shtml.
[6]	BAI Chengzu, ZHANG Ren, ZHANG Jianqi, et al. Risk assessment of typhoon storm surge disasters in Guangdong province based on the improved fuzzy Bayesian network[C]. Proceedings of the 7^th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention, 2016: 217-223.
[7]	张润川, 林广发, 周星辰, 等. 基于动态贝叶斯网络的福建省台风暴雨灾害链预测推理[J]. 亚热带资源与环境学报, 2023, 18 (1):17-25.
	ZHANG Runchuan, LIN Guangfa, ZHOU Xingchen, et al. Prediction and inference of typhoon rainstorm disaster chains in Fujian province based on dynamic Bayesian network[J]. Journal of Subtropical Resources and Environment, 2023, 18(1): 17-25.
[8]	李浩, 李惠. 基于模糊多态贝叶斯网络的山岭隧道塌方风险预测[J]. 项目管理技术, 2023, 21 (10): 54-60.
	LI Hao, LI Hui. Risk prediction of mountain tunnel collapse based on fuzzy polymorphic Bayesian network[J]. Project Management Technology, 2023, 21 (10): 54-60.
[9]	朱庆, 郑威鹏, 吴浩宇, 等. 基于模糊贝叶斯网络的隧道围岩富水破碎风险三维分析方法[J]. 西南交通大学学报, 2025, 60(5):1071-1 079.
	ZHU Qing, ZHENG Weipeng, WU Haoyu, et al. Three-dimensional analysis method for water-rich and broken risk in tunnel surrounding rock using fuzzy Bayesian network[J]. Journal of Southwest Jiaotong University, 2025, 60(5):1071-1 079.
[10]	XIE Xiaoliang, HUANG Linglu, MARSON 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[J]. Natural Hazards, 2023, 117 (3): 3307-3329. doi: 10.1007/s11069-023-05988-x
[11]	王喆, 孔维磊, 方丹辉, 等. 基于贝叶斯网络的城镇洪涝应急情景推演研究[J]. 中国安全科学学报, 2021, 31(6): 182-188. doi: 10.16265/j.cnki.issn 1003-3033.2021.06.024
	WANG Zhe, KONG Weilei, FANG Danhui, et al. Research on urban flood and waterlog emergency scenario deduction based on Bayesian network[J]. China Safety Science Journal, 2021, 31(6): 182-188. doi: 10.16265/j.cnki.issn 1003-3033.2021.06.024
[12]	任永存, 张韧, 张永生, 等. 基于贝叶斯网络的“郑州暴雨地铁灾害事件”情景分析与仿真推演[J]. 大气科学学报, 2023, 46 (6): 904-916.
	REN Yongcun, ZHANG Ren, ZHANG Yongsheng, et al. Scenario analysis and simulation deduction of the "Zhengzhou rainstorm subway disaster event" based on Bayesian network[J]. Transactions of Atmospheric Sciences, 2023, 46(6): 904-916.
[13]	王治莹, 聂慧芳, 赵宏丽. 考虑决策者情绪更新机制的多阶段应急决策方法[J]. 控制与决策, 2020, 35 (2): 436-444.
	WANG Zhiying, NIE Huifang, ZHAO Hongli. Multi-stage emergency decision-making method with emotion updating mechanism of decision-makers[J]. Control and Decision, 2020, 35 (2): 436-444.
[14]	TANG Lan, ZHOU Jiawen, ZHOU Langyu, et al. Exploring the effects of perception factors on evacuation intentions of residents[J]. Journal of Mountain Science, 2025, 22 (2): 1-19. doi: 10.1007/s11629-024-9211-x
[15]	晋泽倩, 俞诚成. 混合效用和后悔规则下的洪涝交通疏散决策行为研究[J]. 交通运输系统工程与信息, 2025, 25(2): 16-25.
	JIN Zeqian, YU Chengcheng. Flood evacuation decision-making behavior under hybrid utility and regret rules[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 16-25.
[16]	霍非舟, 范丹丹, 刘昶, 等. 考虑结伴行为与情绪感染的人员疏散模型[J]. 中国安全科学学报, 2023, 33 (11): 126-132. doi: 10.16265/j.cnki.issn1003-3033.2023.11.2286
	HUO Feizhou, FAN Dandan, LIU Chang, et al. Study on evacuation model considering companion behavior and emotion contagion[J]. China Safety Science Journal, 2023, 33(11): 126-132. doi: 10.16265/j.cnki.issn1003-3033.2023.11.2286
[17]	YE Ran, XU Shuangyan, LIU Juan, et al. Investigation and simulation of non-adaptive psychology of crowd evacuation under toxic gas leakage accident in chemical park[J]. Journal of Loss Prevention in the Process Industries, 2023, 85: DOI:10.1016/j.jlp.2023.105160.
[18]	耿增显, 陈俊宇. 基于模糊贝叶斯网络的低空无人机运行风险评估[J]. 中国安全科学学报, 2024, 34 (8): 53-60. doi: 10.16265/j.cnki.issn1003-3033.2024.08.1295
	GENG Zengxian, CHEN Junyu. Risk assessment of low-altitude unmanned aerial vehicle operation based on fuzzy Bayesian network[J]. China Safety Science Journal, 2024, 34(8): 53-60. doi: 10.16265/j.cnki.issn1003-3033.2024.08.1295
[19]	RADERMACHER J F. Probabilistic reasoning in intelligent systems: networks of plausible inference (judea pearl)[J]. SIAM Review, 2006, 32 (4): 704-707. doi: 10.1137/1032155
[20]	李郎达. 模糊理论在信息检索上的应用[J]. 情报科学, 2004, 22(3): 343-345.
	LI Langda. Application of fuzzy theory in information retrieval[J]. Journal of the China Society for Scientific and Technical Information, 2004, 22(3): 343-345.
[21]	林志军, 李敏, 贺珊, 等. 基于博弈论-贝叶斯网络的煤矿瓦斯爆炸风险评估[J]. 煤炭学报, 2024, 49 (8): 3484-3497.
	LIN Zhijun, LI Min, HE Shan, et al. Risk assessment of gas explosion in coal mines based on game theory and Bayesian network[J]. Journal of China Coal Society, 2024, 49(8): 3484-3497.
[22]	张江石, 冯娜娜. 基于动态贝叶斯网络情景推演的危化品事故应急处置研究[J]. 安全与环境学报, 2020, 20 (4): 1420-1426.
	ZHANG Jiangshi, FENG Na'na. On the emergency disposal of the hazardous chemical accidents based on the dynamic Bayesian network scenario deduction[J]. Journal of Safety and Environment, 2020, 20(4): 1420-1426.
[23]	宋英华, 刘子奇, 刘丹, 等. 基于模糊贝叶斯网络的化工园区火灾爆炸事故情景推演[J]. 安全与环境工程, 2022, 29 (3): 86-93.
	SONG Yinghua, LIU Ziqi, LIU Dan, et al. Scenario deduction of fire and explosion accidents in chemical industry parks based on fuzzy Bayesian network[J]. Safety and Environmental Engineering, 2022, 29 (3): 86-93.
[24]	李博文, 张印, 杨芳, 等. 基于复杂网络模型的广州市增城区洪涝灾害风险评估[J]. 人民珠江, 2023, 44 (10): 47-53.
	LI Bowen, ZHANG Yin, YANG Fang, et al. Flood risk assessment for Zengcheng district of Guangzhou city based on complex network models[J]. Pearl River, 2023, 44 (10): 47-53.
[25]	凉山州金阳县“8·21”山洪灾害调查评估报告公布[N]. 四川日报, 2024-02-06(001).

节点变量	状态	案例分析统计法	三角模糊数法	α₁	α₂ =1-α₁	综合概率
X₁	1	0.624	0.886	0.326	0.674	0.800
X₂	1	0.565	0.812	0.222	0.778	0.757
X₃	1	0.529	0.721	0.135	0.865	0.695
E	孕灾环境是灾害发生的必要条件,因此,只考虑E存在的情况,P(S\|E=0)=0, P(E=1)=1
M	若采取应急措施,则P(M=1)=1;若没有,则P(M=0)=1
S₁=1 (E₁=1)	M₁=1, X₁=1	1.000	0.892	0.507	0.493	0.947
	M₁=1, X₁=0	0.875	0.899	0.496	0.504	0.887
	M₁=0, X₁=1	1.000	0.396	0.715	0.285	0.828
	M₁=0, X₁=0	0.792	0.397	0.688	0.312	0.669
S₂=1 (E₂=1)	S₁=1, M₂=1, X₂=1	1.000	0.771	0.544	0.456	0.896
	S₁=1, M₂=1, X₂=0	1.000	0.770	0.545	0.455	0.895
	S₁=1, M₂=0, X₂=0	0.667	0.618	0.573	0.427	0.646
	S₁=1, M₂=0, X₂=1	0.929	0.700	0.589	0.411	0.835
	S₁=0, M₂=0, X₂=0	0.000	0.120	0.509	0.491	0.059
	S₁=0, M₂=0, X₂=1	0.000	0.228	0.544	0.456	0.104
	S₁=0, M₂=1, X₂=1	0.000	0.261	0.562	0.438	0.114
	S₁=0, M₂=1, X₂=0	0.000	0.093	0.505	0.495	0.046

等级	语言描述	三角模糊数
1	非常高	(0.85,0.925,1)
2	高	(0.75,0.8,0.85)
3	略高	(0.55,0.65,0.75)
4	中等	(0.45,0.5,0.55)
5	略低	(0.25,0.35,0.45)
6	低	(0.05,0.15,0.25)
7	非常低	(0,0.025,0.05)