Rockburst Damage Scale Prediction in Underground Mines Using SMOTE-Based Resampling and Ensemble Learning
Rockburst risk in seismically active mines poses a significant threat to underground safety. This study aims to improve the prediction of rockburst-induced damage by addressing the challenge of class imbalance, which is commonly encountered in rockburst datasets. Various data-balancing techniques, including the Synthetic Minority Oversampling Technique (SMOTE) and its variants, namely SMOTE-Tomek, KM-SMOTE, SMOTE-ENN, SVM-SMOTE, Borderline-SMOTE, and Adaptive Synthetic (ADASYN) sampling, were applied to a dataset containing rockburst damage scales and selected influencing parameters. The data were collected from Canadian and Australian underground mining operations affected by mining-induced seismicity. Three machine learning classifiers, namely Random Forest (RF), CatBoost (CB), and Gradient Boosting (GB), were trained and evaluated using accuracy, recall, precision, and F1-score metrics. The five best-performing models were SMOTE-ENN-GB, SMOTE-ENN-CB, SMOTE-ENN-RF, KM-SMOTE-CB, and Borderline-SMOTE-CB, achieving testing accuracies ranging from 70% to 88%. SHAP analysis further revealed that stress conditions and peak particle velocity (PPV) are the dominant factors controlling rockburst severity, while geological factors and support conditions act as secondary contributing factors. Compared with previous studies using the same dataset, the proposed approach achieved substantial improvements in predictive performance, particularly for the minority and severe rockburst classes. It is concluded that SMOTE-based balancing techniques, when combined with ensemble learning algorithms, can significantly improve rockburst damage prediction and contribute to safer and more effective risk management in deep, seismically active mining environments.