Landslide Susceptibility Assessment in the Upper Minjiang River: A Random Forest Approach Based on Slope Unit

In a high-mountain gorge region, landslide hazards pose a serious threat to the upper Minjiang River, located at the eastern edge of the Tibetan Plateau. To map susceptibility in the upper Minjiang River basin, this study used a Random Forest model in conjunction with slope unit subdivisions. First, a landslide inventory containing 3785 landslides was established using human–machine interactive interpretation techniques. After a multicollinearity analysis, 11 key conditioning factors were selected to construct a spatial database, including elevation, slope, aspect, curvature, topographic wetness index, stream power index, distance to fault, peak ground acceleration, distance to road, vegetation index, and rainfall. The r.slopeunits algorithm was implemented to partition the study area into discrete slope units. The ideal parameter combination for slope units was determined through integrating the normalized slope aspect standard deviation and Moran’s I using an equal-weight scheme. Ultimately, 30,513 slope units were delineated in the upper Minjiang River. The random forest model trained on these ideal slope units was validated using a 70/30 split of landslide and non-landslide samples. In receiver operating characteristic (ROC) curve analysis, the model demonstrated excellent performance, with an area under the curve (AUC) of 0.852. The results indicate that small-scale landslides dominate the inventory in terms of frequency. Despite accounting for only 30% of the study area, the Very High and High susceptibility zones exhibit considerable degree of spatial overlap with current landslide clusters. Furthermore, shapley additive explanations (SHAP) explanatory metrics indicate that the random forest model’s predictive behavior is primarily influenced by terrain elevation, precipitation patterns, and proximity to transportation networks.