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.

Maintaining regional landscape ecological stability and enhancing rural landscape ecosystem services are critical research priorities. This study selected Xiangxi Autonomous Prefecture (XXAP), a representative mountainous region in Southwestern (SW) China, as the case study area. This study aims to construct a rural landscape ecological risk (RLER) evaluation index system based on five-period remote sensing data (2000–2020), analyze the spatio-temporal characteristics of RLER, and provide a scientific basis for landscape ecological management and rural spatial governance. The results show that the RLERI exhibited a balanced multi-ring development trend, decreasing slightly from 0.295 in 2000 to 0.282 in 2020, suggesting a slight alleviation of overall risk. Medium-risk areas of the RLERI consistently accounted for the largest proportion (over 37%). Notably, the share of high-risk areas remained relatively stable, fluctuating narrowly between 7.98% and 8.73%. Meanwhile, high-risk areas of the landscape disturbance degree (LDD) expanded markedly from 1.87% to 10.28%. Correspondingly, high-risk areas of the landscape fragility degree (LFD) also increased significantly, rising from 0.93% to 2.8%. Spatially, RLER displayed significantly positive spatial autocorrelation, with high–high (H-H) clusters concentrated in the central-southern part and low–low (L-L) clusters distributed in the northern and southern margins, indicating pronounced spatial differentiation. In conclusion, this study provides a transferable framework for ecological risk assessment in mountainous regions. Furthermore, it underscores the importance of optimizing landscape patterns in ecologically fragile areas, strengthening ecological risk management, and mitigating ecological risks in rural settings.