## 2 Theoretical Framework and Methods
### Subway Station Flood Resilience System
#### 2.1.3 Resilience Framework for Flooding at Subway Stations  
This study primarily identifies the factors influencing the resilience of subway stations to flood disasters by reviewing and referring to relevant literature, combining actual cases of subway station flood disasters, and conducting expert interviews. These experts include one engineer from the construction department of a rail transit company under China Railway Construction Corporation, one engineer from the operation department of Beijing Rail Transit, one engineer from the Beijing Flood Control Command, and two professors from universities. According to disaster system theory, the formation and evolution of a disaster are the result of the combined effects of the hazard-pregnant environment, disaster-inducing factors, and the disaster-bearing body within a specific spatial context [10,11]. In the study of subway flood disasters, scholars have conducted extensive research from the perspective of traditional disaster risk. Among them, based on the Pressure-State-Response (PSR) theory, an evaluation index system is constructed, and methods such as the entropy weight cloud model [12], DNN neural network model [13], IOWA operator, and vector angle cosine method [14] are used to assess the risk and vulnerability of subway waterlogging. Some scholars have analyzed the vulnerability of subway projects to waterlogging using projection pursuit methods and particle swarm optimization [15]. In addition, interval FAHP-FCA methods and AHP combined with GIS technology [16] are used to assess the flood risk of subway systems. Other studies include the proposal of evidence-based reasoning methods to measure the worst-case flood risk and identify key influencing factors [17]. By considering flood hazards, subway travel exposure, and population vulnerability, a comprehensive assessment of subway flood disaster risk is conducted [18]. Some scholars have also used improved trapezoidal fuzzy analytical hierarchy process [19] and fuzzy analytical hierarchy process (FAHP) combined with geographic information systems (GIS) [20] to assess the flood risk of urban subway systems. These research outcomes provide a scientific basis and effective tools for the assessment and management of flood risks in subway systems. Resilience theory has been applied in research on flood disasters. Some studies have comprehensively analyzed the issues faced by old urban communities in the face of flood disasters [21] based on the connotation, characteristics, and mechanisms of action of urban disaster resilience and combined with the causal logic of the \"Pressure-State-Response\" model, effectively assessed the resilience of mountain towns to disasters [22]. Others have conducted research on the resilience of cities in Hubei Province to flood disasters, aiming to accelerate the construction of resilient cities in the region and promote the sustainable and healthy development of cities [23]. In addition, a resilience assessment method for urban flood disasters based on a combined weighting-cloud model has been proposed using resilience theory [24]. There are also studies that explore the intelligent transformation and implementation path of urban flood resilience governance systems, aiming to enhance the city's ability to cope with flood disasters [25]. These studies together have promoted the scientific and systematic assessment and management of urban flood disaster resilience. How to enhance the resilience of subway stations in the face of flood disasters has become an urgent issue that needs to be addressed. To effectively guard against the risks of subway flood disasters and ensure the safety of the subway system, it is necessary to use scientific methods to objectively assess the resilience status of the subway system, combining the resilience process and the disaster development process and fully considering the subway station's response to flood disasters. Referring to the above literature, a framework for the analysis of subway station flood disaster resilience is constructed.