Improving Landslide Susceptibility Assessment Through Non-Landslide Sampling Strategies

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 RF_IV 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.