Attention-Driven Hierarchical Spatial Adaptive Ensemble for Landslide Susceptibility Mapping.
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| Title: | Attention-Driven Hierarchical Spatial Adaptive Ensemble for Landslide Susceptibility Mapping. |
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| Authors: | Deng, Xuanlun1 (AUTHOR), Li, Yimin2 (AUTHOR) liym@ynu.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1999. 29p. |
| Subjects: | Ensemble learning, Spatial variation, Artificial neural networks, Landslide hazard analysis, Regression analysis |
| Abstract: | Highlights: What are the main findings? Attention-driven adaptive fusion of GWR, GOS, and DNN learns spatially varying base-learner weights, directly overcoming the fixed-weight and kernel-constrained limitations of conventional ensembles. The attention-based ensemble dynamically allocates location-specific fusion weights conditioned on multi-scale environmental features, enabling the ensemble process to adapt to spatially varying model reliability without any predefined spatial kernel or global stationarity assumption. What are the implications of the main findings? Explicitly learning spatial heterogeneity during ensemble fusion is essential for reliable landslide susceptibility mapping, and the framework is readily transferable to other geospatial prediction tasks plagued by non-stationarity. The HSE framework produces geomorphologically refined susceptibility maps that avoid over-diffusion of high-risk zones and misclassification of stable terrain, directly supporting targeted field investigation, engineering mitigation prioritization, and land-use zoning. 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Attention-driven adaptive fusion of GWR, GOS, and DNN learns spatially varying base-learner weights, directly overcoming the fixed-weight and kernel-constrained limitations of conventional ensembles. The attention-based ensemble dynamically allocates location-specific fusion weights conditioned on multi-scale environmental features, enabling the ensemble process to adapt to spatially varying model reliability without any predefined spatial kernel or global stationarity assumption. What are the implications of the main findings? Explicitly learning spatial heterogeneity during ensemble fusion is essential for reliable landslide susceptibility mapping, and the framework is readily transferable to other geospatial prediction tasks plagued by non-stationarity. The HSE framework produces geomorphologically refined susceptibility maps that avoid over-diffusion of high-risk zones and misclassification of stable terrain, directly supporting targeted field investigation, engineering mitigation prioritization, and land-use zoning. 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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121999 |
