The factors influencing urban pluvial flooding in cities with complex topography, such as hill–basin systems, are highly nonlinear and spatially heterogeneous due to the interplay between rugged terrain and intensive human activities. However, previous research has predominantly focused on plain, mountainous, and coastal cities. As a result, the waterlogging mechanisms in hill–basin areas remain notably understudied. In this study, we developed a geographically explainable artificial intelligence (GeoXAI) framework integrating Geographical Machine Learning Regression (GeoMLR) and Geographical Shapley (GeoShapley) values to analyze nonlinear impacts of flooding factors in Changsha, a typical hill–basin city. The XGBoost model was employed to predict flooding risk (validation AUC = 0.8597, R² = 0.8973), while the GeoMLR model verified stable nonlinear driving relationships between factors and flooding susceptibility (test set R² = 0.7546)—both supporting the proposal of targeted zonal regulation strategies. Results indicated that impervious surface density (ISD), normalized difference vegetation index (NDVI), and slope are the dominant drivers of flooding, with each exhibiting distinct nonlinear threshold effects (ISD > 0.35, NDVI < 0.70, Slope < 5°) that differ significantly from those identified in plain, mountainous, or coastal regions. Spatial analysis further revealed that topography regulates flooding by controlling convergence pathways and flow velocity, while vegetation mitigates flooding through enhanced interception and infiltration, showing complementary effects across zones. Based on these findings, we proposed tailored zonal management strategies. This study not only advances the mechanistic understanding of urban waterlogging in hill–basin regions but also provides a transferable GeoXAI framework offering a robust methodological foundation for flood resilience planning in topographically complex cities.

Background: Physical inactivity is a major health risk worldwide, while walking is one of the most accessible forms of exercise that improves public health and supports sustainable urban mobility. Yet the combined and nonlinear effects of the built environment and seasonal climate on exercise walking in high-density cities remain insufficiently explored. This study aims to uncover these relationships and provide insights for health-oriented and climate-adaptive urban planning. Methods: Crowdsourced walking trajectory data were analyzed for three representative high-density Chinese cities，Beijing, Wuhan, and Guangzhou，covering both summer and winter. A comprehensive variable system was established, incorporating built environment, seasonal climate, and socioeconomic factors. A geographically weighted extreme gradient boosting model was developed with Bayesian optimization and cross-validation to improve robustness. Interpretability was achieved through Shapley Additive Explanations, partial dependence plots, and clustering analysis to identify global and local drivers of walking activity. Results: The geographically weighted extreme gradient boosting model outperformed traditional regression and other machine learning models in prediction accuracy. Walking trajectories showed clear spatial clustering, with central urban cores as hotspots, and seasonal differences most pronounced in Beijing. Walk Score was consistently the most stable and influential factor across cities and seasons. Among climatic variables, air quality and temperature had the strongest impacts, particularly in winter. Variables exhibited three types of nonlinear responses: sustained growth (such as Walk Score and pedestrian street length), threshold-sensitive (such as intersection density and population density), and fluctuating patterns (such as air quality and housing prices). Local cluster analysis revealed three context-specific patterns: environment-driven areas such as parks and campuses, function-driven commercial centers, and structurally imbalanced or transitional zones. Conclusions: Exercise walking in high-density cities is shaped by both seasonal climate variability and spatial heterogeneity of the built environment. Improving pedestrian infrastructure, managing density thresholds, and implementing climate sensitive design can mitigate adverse weather impacts and foster year-round walking. Tailored strategies, including enhancing microclimate resilience in ecological zones, optimizing density and functional mix in commercial districts, and restructuring fragmented large blocks, are essential to create pedestrian friendly, health oriented, and climate adaptive cities.