Understanding Geo-Environmental Factors for Landslide Occurrences Using Geographically Explainable AI: A Case Study from the Northeastern United States

Landslide Susceptibility Assessment (LSA) is essential for disaster mitigation and spatial planning, especially in regions with complex terrain and increasing development pressure. This study focuses on the northeastern U.S., particularly the New York Tri-State Area, where glacially shaped hilly terrain, steep slopes, and variable elevations, combined with frequent extratropical cyclones and intense rainfall, contribute to spatially heterogeneous landslide risk. Given the area’s dense population and economic significance, high-resolution evaluation and management are therefore urgently needed. While machine learning (ML) shows significant potential in LSA, many existing models fail to account for spatial heterogeneity among geo-environmental variables and often lack explainability due to their black-box nature. To address these limitations, we propose a geographically explainable AI framework that combines advanced ensemble machine learning with interpretable techniques to better capture local variability and provide spatial insights into landslide susceptibility. In this study, landslide inventories were compiled from the United States Geological Survey and state agency databases. Multi-source geospatial data, including topographic, hydrological, geological, and anthropogenic factors, were used to develop a high-resolution landslide susceptibility map for the study area based on spatial cross-validation. Results show that the proposed framework outperforms several traditional ML models such as Random Forest and LightGBM in predictive performance. In addition to improved accuracy, the framework captures spatial heterogeneity by combining global and localized models through spatial weighting, allowing it to reflect locally varying patterns of landslide susceptibility. It also identifies the relative influence of landslide contributing factors across different geographic contexts, revealing nonlinear relationships and threshold effects between key variables and landslide occurrences. These capabilities enhance the overall interpretability of LSA. The proposed framework is applicable to regional-scale geohazard assessments and offers valuable insights into resilient land-use planning and adaptive disaster mitigation strategies in the Northeastern U.S.