Evaluation of Short-Term Rockburst Risk Severity Using Machine Learning Methods

Abstract

In deep engineering, rockburst hazards frequently result in injuries, fatalities, and the destruction of contiguous structures. Due to the complex nature of rockbursts, predicting the severity of rockburst damage (intensity) without the aid of computer models is challenging. Although there are various predictive models in existence, effectively identifying the risk severity in imbalanced data remains crucial. The ensemble boosting method is often better suited to dealing with unequally distributed classes than are classical models. Therefore, this paper employs the ensemble categorical gradient boosting (CGB) method to predict short-term rockburst risk severity. After data collection, principal component analysis (PCA) was employed to avoid the redundancies caused by multi-collinearity. Afterwards, the CGB was trained on PCA data, optimal hyper-parameters were retrieved using the grid-search technique to predict the test samples, and performance was evaluated using precision, recall, and F1 score metrics. The results showed that the PCA-CGB model achieved better results in prediction than did the single CGB model or conventional boosting methods. The model achieved an F1 score of 0.8952, indicating that the proposed model is robust in predicting damage severity given an imbalanced dataset. This work provides practical guidance in risk management.