Toward Predicting Slope Stability Hazard Levels Using Ensemble Learning

The present study investigates the application of conventional and ensemble machine learning models for slope stability prediction, which is essential for landslide risk reduction and sustainable infrastructure management. A database containing 627 slope cases was used, including six input variables: unit weight, cohesion, friction angle, slope angle, slope height, and pore pressure ratio. Six machine learning models, namely Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Classification and Regression Tree (CART), and Boosted Tree, were developed and evaluated. The models were assessed using ROC analysis, confusion-matrix-derived metrics, precision–recall analysis, feature importance assessment, and unseen testing cases. The results showed that ensemble-based models provided superior predictive performance compared with conventional machine learning models. Based on ROC analysis, RF achieved the highest ROC-AUC value of 0.93, followed by Boosted Tree and XGBoost with ROC-AUC values of 0.92 and 0.90, respectively. Based on confusion-matrix-derived metrics, Boosted Tree achieved the highest accuracy of 0.862 and F1-score of 0.874, while RF showed comparable performance with an accuracy of 0.857 and F1-score of 0.868. Feature importance analysis indicated that cohesion and unit weight were among the most influential variables affecting slope stability prediction. In addition, the unseen testing cases confirmed the practical generalization capability of the ensemble models, with Boosted Tree and RF achieving accuracies of 0.920 and 0.880, respectively. Overall, the findings demonstrate that ensemble learning models, particularly Boosted Tree and RF, can provide reliable and interpretable decision-support tools for preliminary slope stability assessment and landslide hazard management.