## 4 Conclusions  
In this research, we analyzed the characteristics of flood disasters in subway stations, constructed a measurement index system for the resilience of subway stations to flood disasters based on the resilience theory analysis framework, and introduced uncertainty measure models and obstacle degree models to measure and enhance the resilience of subway stations to flood disasters.  
(1) Firstly, in this study, we analyzed the resilience system of subway station flood disasters. By examining the causes and processes of subway floods, a framework for analyzing the resilience of subway stations to flood disasters was constructed. The resilience of subway stations to flood disasters is reflected in their ability to maintain functionality, recover promptly, and continuously learn and adjust in the face of risk impacts from sudden changes in disaster-inducing factors, instability in the disaster-prone environment, and the vulnerability of the disaster-bearing body. In dealing with flood disasters, subway stations need to possess the following capabilities: first, the ability to resist disasters; second, the ability to recover quickly after a disaster; and third, long-term adaptation and learning mechanisms. Secondly, this study introduced uncertainty theory to measure the resilience of subway stations to flood disasters. By establishing grading standards for the resilience of subway stations to flood disasters, constructing uncertainty measure matrices and functions, and measuring the resilience of Jin'anqiao Subway Station in Beijing, the result was found to be moderate resilience. Thirdly, the obstacle degree model was introduced to identify key obstacle factors, and the results showed that indicators A\({}_{32}\), A\({}_{23}\), A\({}_{33}\), A\({}_{31}\), A\({}_{24}\), and A\({}_{34}\) ranked at the forefront. Finally, based on the resilience evaluation results and obstacle degree analysis, strategies for enhancing resilience were proposed.  
(2) Due to the complexity of factors affecting the resilience of subway stations to flood disasters, the current analysis does not consider the interrelated membership of various indicators. Future research should further improve the indicator system and deeply explore the quantitative relationships between indicators. The identification and analysis of disaster-causing factors are based on relevant literature, expert interviews, and case collection, and the obtained disaster-causing factors are limited, which may not fully reflect the overall picture of the disaster-causing factors of flood disasters in subway stations. Subsequent research should continue to supplement and refine the data samples of disaster-causing factors. This study mainly focuses on flood disasters during the operational period of subway stations, while flood disasters may occur at different stages of subway stations. Therefore, follow-up research can comprehensively analyze the disaster-causing factors of flood disasters in subway stations from multiple stages, such as the construction period and operational period.  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors declare no conflicts of interest.