To address the issues of imbalanced rockburst datasets and the challenges in optimizing model parameters,a pre-dictive model based on K-means SMOTE and an improved dung beetle optimizer(IDBO)algorithm for optimizing random forest(RF)is proposed.Initially,the mechanism of rockburst occurrence is analyzed to construct an indicator system.Subse-quently,the K-means SMOTE algorithm is employed to balance the rockburst dataset,and Robust Standardization is used to eliminate dimensionality.Finally,the Tent chaotic map and a nonlinear decreasing strategy are incorporated to improve the dung beetle optimizer algorithm for optimizing RF hyperparameters,resulting in the establishment of a rockburst intensity pre-diction model(IDBO-RF).The model's effectiveness is verified through comparison with other models.The research find-ings indicate that,following data balancing,the accuracy of various models improves by 10.85％to 16.02％.The designed IDBO-RF prediction model achieves an average accuracy of approximately 94.37％,which is an improvement of about 7.76 percentage point,1.69 percentage point,and 1.11 percentage point over the RF,GWO-RF,and DBO-RF models,respec-tively.The IDBO-RF prediction model attains the highest accuracy of approximately 96.43％,outperforming the RF,GWO-RF,and DBO-RF models.These results can provide reference for solving the problem of rockburst prediction.
data balancingimproved dung beetle optimizer(IDBO)random forestrockburst intensity levelpre-diction model
NETL
NSTL
万方数据
