Against the backdrop of climate change and rapid urbanization, assessing urban flood resilience requires spatially continuous and interpretable approaches capable of capturing nonlinear interactions between natural and human systems. This study proposes a high-resolution framework for mapping urban flood resilience in the built-up areas of Chongqing, China, grounded in the geography–ecology–society–infrastructure systems (GESIS) concept. A Flood Resilience Index is constructed at a 50 m grid resolution using ten core indicators and objective weighting based on combined entropy and coefficient-of-variation methods. Three machine learning models—multilayer perceptron (MLP), random forest, and XGBoost—are then trained to reproduce the resilience surface by integrating these indicators with additional historical flood-exposure variables, with SHAP used for model interpretation. The MLP model achieves the best performance (R2 ≈ 0.78) and generates spatially coherent resilience patterns. Impervious surface fraction and building density exert dominant negative effects, whereas elevation and ecological connectivity contribute positively. The results reveal pronounced nonlinear thresholds in key drivers, indicating that flood resilience cannot be inferred from monotonic factor effects alone. By combining objective weighting, explainable machine learning, and historical exposure information, this framework supports both accurate prediction and policy-relevant interpretation of urban flood resilience for sustainable urban planning in mountainous megacities.
Li et al. (Sat,) studied this question.