Analysis of Slope Stability Based on Four Machine Learning Models

To achieve rapid and precise prediction of slope stability, we propose an intelligent assessment method utilizing machine learning techniques. This approach aims to enhance the precision of slope stability evaluations, facilitating more effective and timely decision-making in geotechnical engineering. By analyzing 188 slope cases from domestic and international sources, we have identified six key feature variables to evaluate the Factor of Safety (FOS) for slope stability assessment. The dataset was established for evaluating slope stability, and to ensure robustness, it was divided into training and testing set using a 5-fold cross-validation approach. Four slope stability prediction models- GBM, SVM, XGB, and RF- were developed using machine learning algorithms. The accuracy of the models in predicting FOS for slopes was assessed using metrics such as MAE, MSE, RMSE, and R2. The best-performing machine learning model, along with the finite element model developed using GeoStudio, was applied to engineering examples to compare their feasibility and efficiency. The research findings demonstrates that the GBM model has a minimal error between the predicted and actual slope FOS, highlighting its high accuracy. The model shows a strong correlation between predicted and actual FOS, indicating its superior performance relative to other models. GBM model and the finite element model align well with the actual field conditions. However, the GBM model stands out due to its higher accuracy and faster computational efficiency. Therefore, the GBM model offers a high degree of fit between the predicted FOS and the actual values, making it well-suited for evaluating slope stability.