Improving Landslide Susceptibility Mapping with IF-KMeans Negative Sampling for Geological Disaster Prevention

Reliable landslide susceptibility mapping (LSM) depends not only on classifier selection but also on the construction of non-landslide samples. Conventional random or buffer-based sampling can retain candidate negatives that are environmentally similar to landslides, increasing label ambiguity and reducing model reliability. This study proposes an IF-KMeans negative sampling framework to refine candidate non-landslide samples for LSM in Wuzhou City, China. Isolation Forest was trained using 395 mapped landslides and then applied to 2000 candidate negative samples to remove samples with high similarity to the landslide feature space; K-Means clustering was subsequently used to stratify the retained candidates and select representative negative samples. The optimized samples were evaluated using six classifiers, including LR, SVM, MLP, RF, XGBoost, and LightGBM, and compared with conventional buffer-based sampling. The IF-KMeans framework consistently improved AUC across the six classifiers, with gains of 0.041–0.081, and the IF-KMeans-RF model achieved the highest AUC of 0.944. Additional diagnostics showed that the IF-removed samples were closer to known landslides in environmental feature space and were located in areas with higher local landslide density, indicating higher potential confusion risk. These findings suggest that positive-sample-guided negative-sample refinement can reduce ambiguity in LSM training data and improve the reliability of susceptibility mapping for geological disaster prevention and risk mitigation.