Feasibility of stochastic gradient boosting approach for predicting rockburst damage in burst-prone mines

Abstract The database of 254 rockburst events was examined for rockburst damage classification using stochastic gradient boosting (SGB) methods. Five potentially relevant indicators including the stress condition factor, the ground support system capacity, the excavation span, the geological structure and the peak particle velocity of rockburst sites were analyzed. The performance of the model was evaluated using a 10 folds cross-validation (CV) procedure with 80% of original data during modeling, and an external testing set (20%) was employed to validate the prediction performance of the SGB model. Two accuracy measures for multi-class problems were employed: classification accuracy rate and Cohen's Kappa. The accuracy analysis together with Kappa for the rockburst damage dataset reveals that the SGB model for the prediction of rockburst damage is acceptable.

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