Prediction of safety factor for slope stability using machine learning models

Abstract

Listen

Translate article

Slope instability is a common geological hazard along mountainous roads in Vietnam, leading to significant damage to infrastructure, traffic disruptions, and loss of life. Predicting slope stability, typically quantified by the Factor of Safety (FS), is challenging due to the complex interactions between geotechnical, topographical, and environmental factors. This study aims to develop efficient and accurate models for predicting the FS of natural slopes using advanced machine learning techniques, including Gradient Boosting (GB), Support Vector Machine (SVM), Multi-layer Perceptron (MLP) Neural Networks, Random Forest (RF), and AdaBoost (AB). 371 slope stability cases were used to create a comprehensive database for model training. Both geotechnical and topographical parameters were considered in the FS prediction process. The performance and reliability of these models were evaluated using standard metrics such as R², MAE, and MSE. The results demonstrated that all models exhibited satisfactory prediction capabilities, with the optimized GB model achieving the highest accuracy (R² = 0.975, MAE = 0.079, and RMSE = 0.120). Additionally, SHAP analysis was employed to assess the importance of input variables in predicting the FS. The findings revealed that slope ratio (X1), slope height (X2), and the number of steeps (X3) were the most influential parameters in the FS prediction.