Geographically Weighted Random Forest Considering Spatial Heterogeneity for Landslide Susceptibility Assessment: A Case Study in Yingjiang County

Landslide susceptibility mapping (LSM) is widely used for identifying potential landslide-prone areas. However, many existing approaches rely on global models that assume spatial stationarity, which limits their ability to capture spatially heterogeneous relationships in complex mountainous regions. To address this issue, this study improved landslide susceptibility evaluation by accounting for spatial heterogeneity using a Geographically Weighted Random Forest (GWRF) model. By allowing the influence of conditioning factors to vary spatially, the proposed method provides a more adaptive representation of landslide susceptibility compared to conventional global models. The GWRF-based evaluation results were compared with those obtained from Random Forest (RF) and XGBoost models to examine relative performance. The study was conducted in Yingjiang County, a landslide-prone mountainous area, using multiple landslide conditioning factors, including topographic and anthropogenic variables such as slope and distance to roads. Landslide susceptibility maps were generated, and the evaluation results were supported by InSAR-derived deformation data, field investigations, and UAV observations. The results indicate that the GWRF model achieved superior overall susceptibility evaluation performance compared to the RF and XGBoost models, with an AUC value of 0.922. Furthermore, compared to global models, the GWRF model revealed more detailed spatial patterns of landslide susceptibility, particularly in high-susceptibility zones. Areas classified as highly susceptible by the GWRF model also demonstrated greater consistency with observed deformation features. These findings highlight the importance of considering spatial heterogeneity in landslide susceptibility evaluation and demonstrate that the proposed GWRF approach is applicable for regional-scale susceptibility assessment in complex mountainous environments.