Forecasting Factor of Safety of Slopes Stability Using Several Machine Learning Techniques

Abstract

Because of the disasters associated with slope failure, the analysis and forecasting of slope stability for geotechnical engineers are crucial. In this work, in order to forecast the factor of safety (FOS) of the slopes, six machine learning (ML) techniques of Gaussian process regression (GPR), support vector regression (SVR), decision trees (DT), long-short term memory (LSTM), deep neural networks (DNN), and K-nearest neighbors (KNN) were performed. A total of 327 slope cases in Iran with various geometric and shear strength parameters analyzed by PLAXIS software to evaluate their FOS, were employed in the models. The K-fold (K=5) cross-validation (CV) method was applied to evaluate the performance of models’ prediction. Finally, all the models produced acceptable results and almost close to each other. However, the GPR model with R2 = 0.8139, RMSE = 0.160893, and MAPE = 7.209772%, was the most accurate model to predict slope stability. Also, the backward selection method was applied to evaluate the contribution of each parameter in the prediction problem. The results showed that all the features considered in this study have significant contributions to slope stability. However, features φ (friction angle) and γ (unit weight) were the most effective and least effective parameters on slope stability, respectively.

Estimation of slope stability using ensemble-based hybrid machine learning approaches

“…Other researchers used a variety of novel soft computing techniques to predict FOS, including Multiple Regression (MR), Genetic Algorithm (GA), Support Vector Machine (SVM), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Extreme gradient boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and hybrid models, Gradient Boosting Decision Tree was used in several applications, and the results were found to be noticeably superior to those attained by employing traditional techniques. (Marrapu and Jakka, 2017;Lin et al, 2018a;Bui et al, 2020a;Huang et al, 2020;Deris et al, 2021;Jingjing et al, 2021;Kardhani et al, 2021;Sina et al, 2021;Christoph et al, 2022;Feezan et al, 2022;Gagan et al, 2022;Gexue et al, 2022;Zhihao and Zhiwei, 2022;Mahmoodzadeh and Mohammadi, 2023;Xu et al, 2023). Arunav Chakraborty and Diganta Goswami (Arunav and Diganta, 2017) carried out their work on slope stability prediction utilizing artificial neural networks, very advanced modeling methods that can be suitable for modeling highly complicated functions.…”