Construction of a Two-Stage Rockburst Warning Model Based on Multi-Source Rockburst Case Studies

Rock burst is a sudden disaster that is influenced by various factors. Accurately identifying and predicting rock burst risks is of great significance for improving mine safety. In order to improve the accuracy of rock burst prediction from the perspective of data structure, this paper constructs a two-level prediction model based on six rock burst features. The first-level model uses Box-Cox, Yeo-Johnson, and uniform transformations for data scaling and extends the data using the CTGAN architecture, followed by feature dimension optimization. The second-level model uses the K-Means algorithm to reconstruct labels and enhance inter-class differences, and visualizes clustering effects using ISOMAP. At the algorithm optimization level, an ensemble model stacked with 8 algorithms and a deep forest are used for prediction. The results show that data transformation, increasing model complexity, and appropriate feature expansion can effectively improve prediction accuracy. The single model achieved a maximum accuracy of 81.25%, and the established two-level model outperformed a single machine learning method, with an accuracy improvement of 17.3%. Feature dimension optimization had the highest accuracy improvement of 6.3%. Through comparison, it was found that the deep forest has a prediction accuracy of 98.6%, which is superior to other models such as Gradient Boosting and Multilayer Perceptron. In addition, the SHAP value and 7 evaluation indicators were used to evaluate the model and further explain the prediction results. The proposed two-level rock burst prediction model provides a certain reference value for accurately predicting rock bursts.