Geographically Weighted Random Forest Based on Spatial Factor Optimization for the Assessment of Landslide Susceptibility

Abstract

Landslide susceptibility mapping is a crucial tool for landslide disaster risk management. However, the spatial heterogeneity of landslide conditioning factors affects the accuracy of predictions. This study proposes a novel method combining GeoDetector and geographical weighted random forest (GeoD-GWRF), a local machine learning approach. The GeoD-GWRF model can select landslide conditioning factors from the perspective of spatial differentiation and interpret the influence of factors on landslides at a local scale. The model’s applicability is verified using Luhe County, Guangdong Province, as a case study. Compared to the traditional random forest model, the GeoD-GWRF model achieves higher prediction accuracy (AUC = 0.942). In addition, the model is applicable to broader study areas and can provide more targeted prediction results. This method offers a valuable reference for exploring spatial heterogeneity in landslide susceptibility mapping.