Risk prediction of coal mine rock burst based on machine learning and feature selection algorithm

With the intensification of coal mining in China, rock burst hazards have become increasingly frequent and serious. To comprehensively investigate the accident indicators during mining activities, identify the key triggering factors, and better prevent rockburst accidents, this study establishes a rockburst risk prediction index system containing 16 indicators in four subsystems: geological structure, mining technology, safety management, and employee situation. By using 118 cases as sample data, this study proposes to apply four feature selection methods (Boruta, PC, RF, BE) to reveal the relationship between these indicators and select the most salient indicators for risk prediction. Then four machine learning algorithms, namely Random Forest (RF), Gradient Boosting Machine (GBM), SVM, and KNN are used to compare the effects of different feature selection methods on the prediction accuracy of risk level. The results show that Boruta performs best as a feature selection method for RF, GBM, and KNN, and the Boruta-RF model has better predictive performance than the others.