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 Synthet...