Simulation-Informed Machine Learning for Multiclass Landslide and Debris-Flow Hazard Classification in a Recently Urbanized City, Republic of Korea

Rapid urban expansion into mountainous terrain, together with increasing exposure to extreme weather, has increased the need for transparent and interpretable slope-hazard assessment in forested and residential mountain environments. However, relatively few studies have integrated field-survey information with physically based simulation outputs for interpretable multi-class classification within an established slope-hazard grading framework. This pilot study developed a simulation-informed machine-learning framework for classifying composite slope-hazard grades (Grades I–III) using 71 landslide-prone and 52 debris-flow-prone sites in Sejong City, Republic of Korea. For the landslide dataset, selected field-survey indicators were integrated with SoilWorks-derived slope-stability information, whereas for the debris-flow dataset, selected field-survey indicators were integrated with FLO-2D-derived simulation information. Four machine-learning algorithms—Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—were evaluated under severe class imbalance. RF showed the strongest overall performance in the original holdout evaluation, achieving an accuracy of 0.857 for both datasets and weighted F1-scores of 0.849 for the landslide dataset and 0.836 for the debris-flow dataset. Repeated stratified train–test analyses further showed that RF retained the highest mean overall performance, although the debris-flow dataset was more sensitive to sample allocation. Gini importance, Shapley Additive Explanations (SHAP), and targeted ablation analysis indicated that simulation-derived variables provided strong discriminatory information within the established grading framework. The proposed framework provides a structured and interpretable approach for integrating field-based assessment indicators with physically based simulation outputs and may provide supplementary support for classifying and prioritizing sites requiring further assessment. The findings also provide a basis for future investigation of reduced-input screening approaches, although the present study does not establish that reduced field surveys or simulation outputs alone are sufficient for formal grade assignment. Accordingly, the framework should be interpreted as a supplementary classification approach within the existing assessment system rather than as an independent predictor of future landslide or debris-flow occurrence.