Evaluation of emergency management efficiency for urban flood disaster based on improved grey group decision making

doi:10.16265/j.cnki.issn1003-3033.2025.09.1608

Abstract

Abstract:

To address the issues of preference dependence and loss aversion of decision makers under risk conditions in the evaluation of urban flood disaster emergency management efficiency, an improved grey group decision-making model integrating the risk preference of decision-makers was constructed. Firstly, a decision-maker weight determination method based on group consensus and difference was proposed, which overcame the limitation of traditional method that ignored differences of decision-maker's professional background, experience and personal preference when integrating individual information. Then, the TODIM method was proposed to reduce the problem of loss overestimation faced by decision makers under risk when calculating dominance. Finally, the model was applied to the efficiency evaluation of urban flood disaster emergency management schemes, which verified its effectiveness. The results show that the scheme of improving the emergency response and rescue system is superior to both the early warning and monitoring system construction scheme and information technology support scheme, when comprehensively considering emergency rescue efficiency, disaster prevention and mitigation effect, resource utilization efficiency and emergency management cost. Moreover, this optimal emergency scheme remains robust in 81.82% of the scenarios.

Key words: urban flood disaster, emergency management efficiency, grey group decision-making, risk preference, improved interactive multi-criteria decision-making (TODIM)

CLC Number:

X915.5

SUN Huifang, CHEN Yang, MAO Wenxin. Evaluation of emergency management efficiency for urban flood disaster based on improved grey group decision making[J]. China Safety Science Journal, 2025, 35(9): 220-227.

Figures/Tables 6

Fig.1

Table 1

Table 2

Table 3

Fig.2

Table 4

Comparative analysis results

决策方法	决策结果
文中所提方法	$A 2 A 3 A 1$
经典TODIM	$A 2 A 1 A 3$
文献[17]灰关联决策方法	$A 2 A 3 A 1$
文献[12]群体信息集结方法	$A 2 A 1 A 3$

Table 4

References 19

[1]	李海辰, 冯文文, 张少恺, 等. 城市洪涝灾害防御中的前沿信息技术应用[J]. 中国安全科学学报, 2022, 32(9): 137-143. doi: 10.16265/j.cnki.issn1003-3033.2022.09.2707
	LI Haichen, FENG Wenwen, ZHANG Shaokai, et al. Review on application of cutting-edge information technology in urban flood prevention system[J]. China Safety Science Journal, 2022, 32(9):137-143. doi: 10.16265/j.cnki.issn1003-3033.2022.09.2707
[2]	王永明, 郑姗姗. 地方政府应急管理效能提升的多重困境与优化路径:基于“河南郑州‘7·20’特大暴雨灾害”的案例分析[J]. 管理世界, 2023, 39(3):83-96.
	WANG Yongming, ZHENG Shanshan. Multiple dilemmas and optimization path for improving the efficiency of local government emergency management: case analysis based on henan zhengzhou torrential rain-caused disaster on July 20[J]. Management Word, 2023, 39(3):83-96.
[3]	CHEN Ning, LIU Wenjing, BAI Ruizhen, et al. Application of computational intelligence technologies in emergency management: a literature review[J]. Artificial Intelligence Review, 2019, 52: 2131-2168. doi: 10.1007/s10462-017-9589-8
[4]	EMERGENCY R. Federal emergency management agency[J]. Federal Register, 1996, 61(90):20944-20 970.
[5]	OZGUVEN E E, OZBAY K. An RFID-based inventory management framework for emergency relief operations[J]. Transportation Research Part C: Emerging Technologies, 2015, 57: 166-187.
[6]	CHEN Peng, ZHANG Jiquan, SUN Yingyue. Research on emergency rescue of urban flood disaster based on wargame simulation[J]. Journal of the Indian Society of Remote Sensing, 2018, 46(10): 1677-1687.
[7]	宋英华, 韩保帅, 郭晨. 考虑洪涝灾害风险的县域应急避难场所选址模型[J]. 中国安全科学学报, 2024, 34(4): 191-198.
	SONG Yinghua, HAN Baoshuai, GUO Chen. Location selection model of emergency shelter considering risk of flood disaster in county[J]. China Safety Science Journal, 2024, 34(4): 191-198. doi: 10.16265/j.cnki.issn1003-3033.2024.04.6850
[8]	ZHANG Yang, SHANG Kejian. Cloud model assessment of urban flood resilience based on PSR model and game theory[J]. International Journal of Disaster Risk Reduction, 2023, 97: DOI: 10.1016/j.ijdrr.2023.104050.
[9]	MEHRYAR S, SURMINSKI S. Investigating flood resilience perceptions and supporting collective decision-making through fuzzy cognitive mapping[J]. Science of the Total Environment, 2022, 837:DOI:10.1016/j.scitotenv.2022.155854.
[10]	LI Guangxu, KOU Gang, PENG Yi. Heterogeneous large-scale group decision making using fuzzy cluster analysis and its application to emergency response plan selection[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 52(6): 3391-3403.
[11]	YIN Xuanpeng, XU Xuanhua, PAN Bin. Selection of strategy for large group emergency decision-making based on risk measurement[J]. Reliability Engineering & System Safety, 2021, 208: DOI:10.1016/j.ress.2020.107325.
[12]	SUN Huifang, MAO Wenxin, DANG Yaoguo, et al. Optimum path for overcoming barriers of green construction supply chain management: a grey possibility DEMATEL-NK approach[J]. Computers & Industrial Engineering, 2022, 164: DOI: 10.1016/j.cie.2021.107833.
[13]	XIE Xudong, HU Mingli, YANG Yingjie, et al. Grey target group decision model based on expected intervals of experts[J]. The Journal of Grey System, 2020, 32(4): 77-89.
[14]	孙慧芳, 党耀国, 毛文鑫. 基于前景理论的风险型多阶段灰靶决策方法[J]. 统计与决策, 2020, 36(7):183-188.
	SUN Huifang, DANG Yaoguo, MAO Wenxin. Risk based multistage grey target decision making method based on prospect theory[J]. Statistics and Decision Making, 2020, 36 (7): 183-188.
[15]	姜雨茹, 刘焱, 叶国菊. 基于专家动态赋权的离散模糊语言群决策方法[J]. 西华大学学报:自然科学版, 2024, 43(5): 124-130.
	JIANG Yuru, LIU Yan, YE Guoju. Discrete fuzzy language group decision making method based on expert dynamic weighting[J]. Journal of Xihua University:Natural Science Edition, 2024, 43 (5): 124-130.
[16]	SUN Huifang, DANG Yaoguo, MAO Wenxin. Identifying key factors of regional agricultural drought vulnerability using a panel data grey combined method[J]. Natural Hazards, 2019, 98(2): 621-642. doi: 10.1007/s11069-019-03722-0
[17]	LIU Sifeng, YANG Yingjie, FORREST J. Grey data analysis[M]. Singapore: Springer Singapore, 2017: 234-239.
[18]	KAHNEMAN D, TVERSKY A. Prospect theory: an analysis of decision under risk[M]. Singapore: Handbook of the Fundamentals of Financial Decision Making: Part I. 2013: 99-127.
[19]	闫书丽. 灰靶决策方法及应用研究[D]. 南京: 南京航空航天大学, 2014.
	YAN Shuli. Grey target decision method and its application[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014.

备选方案	方案描述
A₁:早期预警和监测系统建设方案	侧重于建设城市F洪涝灾害早期预警和监测系统,提高对洪涝灾害的感知和应对能力,包括洪水监测设备、气象预警系统、水位水质监测设施等
A₂:应急响应和救援体系完善方案	着重于加强城市F应急响应和救援体系的建设,包括完善灾害应急预案、提升救援队伍能力、强化应急物资储备等
A₃:信息化技术支持方案	采用信息技术支持城市F洪涝灾害的应急管理,包括建设应急管理信息系统、利用大数据和人工智能技术进行灾害预测和应急指挥,提升灾害应对智能化水平

评价指标	对应指标的含义解释
C₁:应急救援效率	指救援行动的速度、有效性和覆盖范围,反映救援队伍和物资的调动和应用效果。主要包括救援队伍到达灾区的时间、救援执行效果、救援物资供应的及时性等
C₂:防灾减灾效果	指应急管理措施对灾害发生和发展的抑制和减轻程度。主要包括防洪设施的稳定性和效果、排水系统的通畅性、应急预案的实施效果等
C₃:资源利用效率	指应急管理中各类资源(人力、物资、设备等)的调配和利用效果,以及衡量资源调配的合理性和利用效果,包括物资储备和调配的时效性、人力资源的优化配置等
C₄:应急管理成本	指实施应急管理方案所需的经济投入和资源消耗。主要包括方案的实施成本、维护成本以及灾后恢复和修复的经济投入等

专家		C₁	C₂	C₃	C₄
e₁	A₁	[82, 85, 88]	[84, 86, 90]	[87, 89, 94]	[390, 420, 460]
	A₂	[86, 89, 94]	[81, 85, 89]	[88, 90, 93]	[320, 360, 410]
	A₃	[84, 87, 91]	[85, 88, 92]	[82, 85, 89]	[370, 410, 470]
e₂	A₁	[85, 88, 90]	[81, 85, 89]	[84, 87, 90]	[350, 370, 420]
	A₂	[84, 86, 91]	[83, 89, 94]	[86, 91, 95]	[280, 330, 380]
	A₃	[86, 89, 93]	[80, 82, 86]	[83, 87, 91]	[260, 300, 350]
e₃	A₁	[80, 83, 87]	[82, 87, 93]	[79, 84, 87]	[360, 400, 470]
	A₂	[87, 90, 94]	[81, 85, 88]	[82, 86, 89]	[300, 340, 370]
	A₃	[82, 86, 89]	[78, 83, 86]	[85, 88, 93]	[330, 380, 440]