A GeoDetector–SHAP coupled explainable machine learning framework for landslide susceptibility mapping

Landslide susceptibility mapping (LSM) is important for hazard prevention and land-use planning. Current machine learning-based LSM studies often lack integrated interpretation of spatial explanation, model contribution and factor interactions. To address this issue, this study developed a GeoDetector–SHAP coupled explainable machine learning framework for LSM and applied it to Jiangxi Province, China. The modeling dataset comprised 156 verified damaging landslides documented during 2019–2020, 468 non-landslide samples, and nine conditioning factors. Four machine learning models were constructed and compared to identify the optimal model for subsequent interpretation and mapping. Based on the optimal model, GeoDetector and SHAP were first used to interpret landslide-controlling factors from spatial explanatory and model-prediction perspectives, respectively, and were then integrated through a coupled explanatory index for comprehensive analysis. To further clarify the interaction mechanisms among these dominant controls, the GeoDetector interaction detector was applied. The coupled results showed that, given the available inventory and analytical design, distance to roads, DEM, and lithology ranked highest. The proposed framework provides a more comprehensive interpretation of landslide-controlling factors and offers useful support for regional landslide susceptibility assessment.