Landslides are a prevalent geological hazard in China, posing significant threats to life and property. Landslide susceptibility assessment is essential for disaster prevention, and the quality of non-landslide samples critically affects model accuracy. This study takes Yongxin County, Jiangxi Province, as a case, selecting ten susceptibility factors and applying the Random Forest (RF) model with six non-landslide sampling methods for comparison. Results indicate that non-landslide sample selection substantially influences model performance, with the RF model using the IV method achieving the highest accuracy (AUC = 0. 9878). SHAP analysis identifies NDVI, slope, lithology, land cover, and elevation as the primary contributing factors. Statistical results show that RFIV non-landslide sample predictions are lowest, mainly below 0. 18, with a median of 0. 18, confirming that the IV method effectively excludes landslide-prone areas and accurately represents non-landslide regions. These findings provide practical guidance for landslide risk managers, local authorities, and policymakers, and offer methodological insights for researchers in geological hazard modeling.
Tu et al. (Wed,) studied this question.