Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data

TLDR

Slope failures cause catastrophic consequences worldwide, making slope stability assessment a critical focus in geotechnical and geological engineering. The study proposes a hybrid stacking ensemble approach to improve slope‑stability prediction. The method trains the ensemble by generating 150 synthetic slope cases via finite element analysis, selecting optimal base classifiers and a meta‑classifier from 11 optimized machine‑learning models using an artificial bee colony algorithm, and evaluating the model on 107 real field cases while comparing its performance to individual and basic ensemble methods. The hybrid stacking ensemble achieved a 90.4 % AUC—7 % higher than the best individual model (82.9 %)—and outperformed a basic ensemble, confirming its superior predictive capability.

Abstract

Slope failures lead to catastrophic consequences in numerous countries and thus the stability assessment for slopes is of high interest in geotechnical and geological engineering researches. A hybrid stacking ensemble approach is proposed in this study for enhancing the prediction of slope stability. In the hybrid stacking ensemble approach, we used an artificial bee colony (ABC) algorithm to find out the best combination of base classifiers (level 0) and determined a suitable meta-classifier (level 1) from a pool of 11 individual optimized machine learning (OML) algorithms. Finite element analysis (FEA) was conducted in order to form the synthetic database for the training stage (150 cases) of the proposed model while 107 real field slope cases were used for the testing stage. The results by the hybrid stacking ensemble approach were then compared with that obtained by the 11 individual OML methods using confusion matrix, F1-score, and area under the curve, i.e. AUC-score. The comparisons showed that a significant improvement in the prediction ability of slope stability has been achieved by the hybrid stacking ensemble (AUC = 90.4%), which is 7% higher than the best of the 11 individual OML methods (AUC = 82.9%). Then, a further comparison was undertaken between the hybrid stacking ensemble method and basic ensemble classifier on slope stability prediction. The results showed a prominent performance of the hybrid stacking ensemble method over the basic ensemble method. Finally, the importance of the variables for slope stability was studied using linear vector quantization (LVQ) method.