Scale Effects in Landslide Susceptibility Assessment: Integrating Slope Unit Division and SHAP−Based Interpretability in a Typical River Basin

Landslide susceptibility assessment (LSA) plays a pivotal role in regional disaster prevention, particularly in southeastern Tibet, where frequent landslides pose significant threats to human safety and critical infrastructure. However, current LSA approaches face two key challenges: (1) the absence of standardized guidelines for selecting appropriate slope unit scales, which may result in over−smoothing or excessive noise in spatial patterns; and (2) the limited interpretability of machine learning models, which hampers understanding of factor contributions. This study investigates the scale effects of slope unit delineation on LSA in the Yuqu River Basin. Using the r.slopeunits method, six datasets at varying scales were generated to capture terrain heterogeneity. An XGBoost−based framework was applied for susceptibility modeling, with SHAP (Shapley Additive Explanations) used to enhance model interpretability. Results indicate that slope unit scale substantially affects sample distribution, feature representation, and model performance. At the smallest scale (c = 0.05), excessive data redundancy and imbalanced class ratios reduced accuracy (AUC = 0.824). At the largest scale (c = 0.5), spatial heterogeneity was over−smoothed, also impairing performance (AUC = 0.832). The intermediate scale (c = 0.3) performed best, yielding a balanced representation and a mean AUC of 0.856. SHAP analysis highlighted freezing index, relative height, and rainfall as the most influential factors. Notably, susceptibility increased significantly when the freezing index ranged between 1500 and 3000 °C·d and relative height between 500 and 1500 m. Additionally, interactions—such as between the freezing index and slope gradient or fault density—further intensified landslide risk, underscoring the need to consider nonlinear dependencies. By integrating multi−scale modeling with SHAP−based interpretation, this study enhances both the predictive accuracy and transparency of LSA.