Explainable AI using ensemble machine learning with integrated SHapley additive explanations (SHAP)-Borda approach for estimation of the safety factor against soil liquefaction

In most of the studies on soil liquefaction prediction based on Machine Learning (ML), the models presented are presented in a closed box structure. In the studies where the effect of the features on the model performance is analyzed with Interpretability methods, it is seen that the order of effect of the features changes for each ML algorithm. This situation makes the results of the studies conducted on the same subject inconsistent. In this study, we propose an integrated SHapley Additive exPlanations (SHAP)-Borda approach to overcome this problem. With this study, we provide decision makers with ease in explaining ML models by combining SHAP analysis results with the Borda method for the first time. In the study, ensemble ML algorithms were used for soil liquefaction prediction using data collected from the literature. The performances of the model predictions obtained by hyper parameterization were compared, and correlation results ranging from 0.91 to 0.93 were obtained. Ensemble ML algorithms that were found to be successful as a result of evaluating other performance criteria were analyzed with the SHAP-Borda approach in the study. It has been observed that with the proposed SHAP-Borda approach, the interpretability results of different ML algorithms can be brought together, and a final result can be presented, providing ease of evaluation for decision makers. The study also shows that (N₁)₆₀ and a_max are the most effective features in predicting soil liquefaction.