Urban Flood Disaster Resilience Evaluation Based on Scenario Construction: A Case Study of the Beijing-Tianjin-Hebei Urban Agglomeration

As the natural disasters and social risks faced by cities become increasingly complex, the enhancement of urban safety and resilience capabilities becomes crucial. This paper elaborates on the concept of resilient cities, compares urban resilience assessment methods based on mathematical analysis and model simulation, and selects the scenario construction method to focus on the typical scenario of rainstorm and flood. It deeply analyzes the disaster mechanism of rainstorm and flood, constructs the typical scenario, and systematically sorts out the tasks at each stage. Based on the “target-task-capability” framework, an evaluation indicator system consisting of 5 first-level indicators and 24 second-level indicators is constructed. This paper uses Python libraries such as Pandas and NumPy to clean data, addressing missing or invalid entries by using the average value of the corresponding province where data is missing. This paper standardizes data using the StandardScaler from Scikit-learn, ensuring consistency across indicators with different units. This paper applies a multi-indicator weighted arithmetic mean aggregation method to calculate the resilience scores, which it then visualizes using Matplotlib and Seaborn. The results show an overall improvement in resilience in the region, though significant disparities exist between cities. This paper provides valuable insights into enhancing urban resilience through efficient data processing and analysis using Python, offering a foundation for future urban disaster preparedness strategies.