Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction

Slope engineering is a complex nonlinear system. It is difficult to respond with a high level of precision and efficiency requirements for stability assessment using conventional theoretical analysis and numerical computation. An ensemble learning algorithm for solving highly nonlinear problems is introduced in this paper to study the stability of 444 slope cases. Different ensemble learning methods [AdaBoost, gradient boosting machine (GBM), bagging, extra trees (ET), random forest (RF), hist gradient boosting, voting and stacking] for slope stability assessment are studied and compared to make the best use of the large variety of existing statistical and ensemble learning methods collected. Six potential relevant indicators, γ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}, C, φ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varphi$$\end{document}, β\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document}, H and ru\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r_{u}$$\end{document}, are chosen as the prediction indicators. The tenfold CV method is used to improve the generalization ability of the classification models. By analysing the evaluation indicators AUC, accuracy, kappa value and log loss, the stacking model shows the best performance with the highest AUC (0.9452), accuracy (84.74%), kappa value (0.6910) and lowest log loss (0.3282), followed by ET, RF, GBM and bagging models. The analysis of engineering examples shows that the ensemble learning algorithm can deal with this relationship well and give accurate and reliable prediction results, which has good applicability for slope stability evaluation. Additionally, geotechnical material variables are found to be the most influential variables for slope stability prediction.