Interpretable Ensemble Machine Learning for Liquefaction Risk Prediction
Tuzelbayev D. Moon S.-W. Lee M. Abdialim S. Aremu E.A. Satyanaga A. Kim J.
November 2025Multidisciplinary Digital Publishing Institute (MDPI)
Infrastructures
2025#10Issue 11
This paper presents a comprehensive machine learning (ML) framework for predicting liquefaction risk, a crucial aspect of seismic hazard assessment. A benchmark geotechnical dataset with multi-dimensional input features was used to evaluate several ML classifiers, followed by hyperparameter optimization through stratified 5-fold cross-validation. Optimized models were combined into a soft Voting Ensemble to enhance stability and accuracy of liquefaction potential prediction. The proposed ensemble model achieved a mean accuracy of 90.12% and a recall of 97.23%, outperforming individual models in most folds. The ensemble’s effectiveness was further evidenced by its precision-recall (PR) and receiver operating characteristic (ROC) curves, with areas under the curve (AUC) of 0.962 and 0.931, respectively—closely matching those of the Gradient Boosting classifier, indicating comparable discriminatory performance. Additionally, SHapley Additive exPlanations (SHAP) analysis was conducted on the ensemble model to assess contributions of each geotechnical inputs to the predictions, revealing that normalized shear wave velocity ((Formula presented.)) as the most influential variable in liquefaction prediction. The proposed framework demonstrates a robust, interpretable, and performance-consistent approach for liquefaction risk assessment.
ensemble learning , hyperparameter optimization , liquefaction , machine learning , SHAP analysis
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Department of Civil and Environmental Engineering, Nazarbayev University, 53 Kabanbay Batyr Ave, Astana, 010000, Kazakhstan
Department of Computer Science, Nazarbayev University, 53 Kabanbay Batyr Ave, Astana, 010000, Kazakhstan
Department of Civil and Environmental Engineering
Department of Computer Science
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