Resilience Assessment Method of Urban Flooding Prevention and Control System (FPC) Based on Attribute Resilience (AR) and Functional Resilience (FR)

Under the context of global climate change, floods are one of the major challenges facing urban development. Based on resilience theory, this study proposed an evaluation method to accurately assess the resilience of urban flooding prevention and control systems (FPCs), integrating both attribute resilience (AR) and functional resilience (FR). First, the method organized FPC attributes from the perspective of the waterlogging generation and elimination processes using foundational data from the study area, and it established a resilience indicator system. The Entropy Weight Method (EWM) was applied to calculate indicator weights, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to calculate indicator values, ultimately deriving the attribute resilience (AR). Subsequently, functional performance during actual operations was evaluated using scenario simulation based on hydrodynamic model results, and the FR was determined. Finally, spatial correlation analysis of the AR and FR was conducted to identify areas with weak resilience. This study developed an evaluation method that considers both system attributes and functional performance using the central urban area of Beijing as a case study to assess flood resilience. The results indicated that the most influential factors affecting the AR of the FPC are the green space percentage (GSP), average slope, and drainage capacity (DC), with their weights calculated as 0.17, 0.137, and 0.205, respectively. Among resistance, absorption, and recovery, absorption had the greatest influence, with a weight of 0.447. The Moran’s I indices for the AR and FR were 0.66 and 0.49, respectively, indicating spatial clustering, although the clustering locations differed. There was spatial correlation between the AR and FR, enabling more precise identification of areas with high and low flood resilience. However, the trends of the AR and FR were not entirely consistent across different types of sub-districts due to differences in evaluation methods and the influence of various indicators.