A comprehensive evaluation of ensemble machine learning in geotechnical stability analysis and explainability

Abstract

We investigated the application of ensemble learning approaches in geotechnical stability analysis and proposed a compound explainable artificial intelligence (XAI) fitted to ensemble learning. 742 sets of data from real-world geotechnical engineering records are collected and six critical features that contribute to the stability analysis are selected. First, we visualized the data structure and examined the relationships between various features from both a statistical and an engineering standpoint. Seven state-of-the-art ensemble models and several classical machine learning models were compared and evaluated on slope stability prediction using real-world data. Further, we studied model fusion using the stacking strategy and the performance of model fusion that contributes to slope stability prediction. The results manifested that the ensemble learning model outperformed the classical single predictive models, with the CatBoost model yielding the most favourable results. To dive deeper into the credibility and explainability of CatBoost composed of multiple learners, the compound XAI fitted to CatBoost was formulated using feature importance, sensitivity analysis, and Shapley additive explanation (SHAP), which further strengthened the credibility of ensemble learning in geotechnical stability analysis.

Graphical abstract