A BSMOTE-OOA-SuperLearner Hybrid Framework for Interpretable Prediction of Pillar Stability

Pillar stability prediction is essential for underground mining safety, yet it remains challenging due to limited data, class imbalance, and insufficient interpretability. This study proposes an integrated Borderline-SMOTE-Osprey Optimization Algorithm-Super Learner framework (BSMOTE-OOA-SL) for hard-rock pillar stability prediction. The framework combines five heterogeneous base learners (ANN, GBDT, KNN, RF, and SVM), applies Borderline-SMOTE within training folds to alleviate class imbalance, and employs the Osprey Optimization Algorithm (OOA) for systematic hyperparameter optimization. The model is evaluated using a dataset of 241 pillar cases from seven underground mines. Statistical experiments based on multiple random train–test splits show that the proposed framework consistently outperforms individual base learners in terms of Accuracy, Macro-Precision, Macro-Recall, and Macro-F1, demonstrating improved robustness and generalization. Ablation results indicate that the joint use of Borderline-SMOTE and OOA leads to quantitative performance gains of 10.21%, 12.25%, 12.61%, and 12.86% in Accuracy, Macro-Precision, Macro-Recall, and Macro-F1, respectively. Under a representative data split, the model achieves an overall accuracy of 95.92%, with strong class-wise Precision, Recall, and F1-score across all stability categories, and AUC values exceeding 0.9 for all classes (reaching 1.0 for the Failed category). SHAP-based interpretability analysis identifies stress-related indicators—particularly average pillar stress, Stress/UCS ratio, and UCS—as the dominant factors governing pillar stability. Overall, the proposed BSMOTE-OOA-SL framework provides a robust, interpretable, and statistically reliable solution for hard-rock pillar stability prediction.