Attention-Driven Hierarchical Spatial Adaptive Ensemble for Landslide Susceptibility Mapping

Landslides cause thousands of fatalities and billions in economic losses annually, yet reliable susceptibility mapping across heterogeneous landscapes remains challenging because conventional models assume stationary relationships between landslide occurrence and environmental controls. Ensemble methods, though promising, rely on either globally fixed aggregation weights or kernel-constrained local averaging, failing to adapt when the reliability of base models varies nonlinearly across space. To overcome this, we propose a two-stage hierarchical spatial adaptive ensemble (HSE) framework. In stage one, three complementary base learners are deployed: geographically weighted regression (GWR) for local spatial non-stationarity; a geographically optimal similarity (GOS) model, grounded in the Third Law of Geography, to represent similarity-based local dependence; and a deep neural network (DNN) for nonlinear covariate interactions. In stage two, a multi-branch attention-based network learns spatially varying fusion weights via multi-scale feature extraction, abandoning fixed weights or kernel constraints. We validate HSE on a typical landslide-prone catchment, comparing against single models (GWR, DNN, GOS). Results demonstrate that our method consistently achieves superior predictive accuracy, spatial consistency, and out-of-sample robustness. Moreover, the attention-derived spatially varying weights provide interpretable insights into where each base learner dominates, bridging predictive performance with geophysical interpretability. These findings confirm that explicitly learning spatial heterogeneity during ensemble fusion is essential for reliable landslide susceptibility mapping, with strong potential for transfer to other geospatial prediction tasks.