FLOOD RISK ASSESSMENT AND RESILIENCE STRATEGIES FOR URBAN METROS UNDER EXTREME RAINFALL

АУТОР(И) / AUTHOR(S): Shuguang Liu, Weiqiang Zheng, Guihui Zhong, Zhengzheng Zhou

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DOI:  10.46793/ACUUS2025.3.8.96

САЖЕТАК / ABSTRACT:

Shanghai, located at the Yangtze River estuary, is one of China’s fastest-growing cities in underground transportation and also highly vulnerable to flood disasters. With extreme weather events becoming more frequent, the city’s metro system faces increasing flood risks. This study selects eight indicators—including rainfall intensity, terrain slope, and passenger volume—and uses the entropy weight method to assign indicator weights. A flood risk assessment framework is developed and applied to 282 metro stations in Shanghai’s central urban area. The spatial distributions of hazard, exposure, vulnerability, and defense indices are analyzed, and stations are classified into different risk zones. Based on the assessment, targeted disaster mitigation strategies are proposed to enhance flood resilience. Results indicate that flood risk generally decreases from northeast to southwest, and the spatial pattern of overall risk aligns closely with hazard levels. Stations in the northeastern region show high hazard and require improved responses to extreme rainfall. Central stations, characterized by high exposure and weak mitigation capacity, need enhanced surface and drainage infrastructure. Southwestern and southeastern stations show high vulnerability, highlighting the need for better emergency preparedness. The findings contribute to resilience-oriented urban planning and provide technical support for sustainable development in flood-prone metropolitan areas.

КЉУЧНЕ РЕЧИ / KEYWORDS:

Flood risk assessment; Indicator system; Metro stations; Shanghai; Resilience-based mitigation

ПРОЈЕКАТ / ACKNOWLEDGEMENT:

This study was supported by the National Natural Science Foundation of China (Grant Nos. 42271031 and 42371030). The authors sincerely appreciate the assistance from Runyao Lin (College of Environmental Science and Engineering, Tongji University), Congju Liu (College of Civil Engineering, Tongji University), and Zhi Li (College of Civil Engineering, Tongji University), for their contributions to data collection from Baidu Maps. The authors sincerely appreciate the valuable feedback provided by the reviewers, whose insightful comments have significantly contributed to enhancing the quality of this paper.

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