Prediction of safety factor for slope stability using machine learning models

Nguyen Dam Duc; Manh Duc Nguyen; Indra Prakash; Huong Nguyen Van; Hiep Van Le; Pham Binh Thai

doi:10.15625/2615-9783/22196

Author affiliations

Authors

Dam Duc Nguyen University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, Viet Nam
Manh Duc Nguyen University of Transport and Communications, Hanoi, Vietnam
Indra Prakash DDG(R) Geological Survey of India, Gandhinagar, Gujarat, 382010, India
Nguyen Van Huong Hanoi University of Natural Resources and Environment, Hanoi, Vietnam
Hiep Van Le University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, Viet Nam
Binh Thai Pham University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Hanoi, Vietnam

DOI:

https://doi.org/10.15625/2615-9783/22196

Keywords:

Slope instability, soft computing, factor of safety, gradient boosting, Vietnam

Abstract

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.

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