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

随机梯度提升方法预测有岩爆倾向矿山岩爆破坏的可行性（英文）

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.

基于254个岩爆破坏事件数据库,采用随机梯度提升方法(SGB)对岩爆破坏进行分类检验评估。SGB方法中选取5个可能性相关指标进行评价,包括应力条件因素、地下支护能力、地质构造以及岩爆发生场地质点峰值振动速度等指标。模型在评价过程中选取80%的原始数据进行建模并使用10倍交叉验证方法评估模型的性能,然后进行外部测试,用剩余20%的数据检验SGB模型的预测准确性。对于多类问题模型准确性分析采用分类准确率和科恩Kappa系数两种准确性方法。对岩爆破坏的数据准确性分析和Kappa系数的分析表明SGB模型分析法对于岩爆破坏预测是可靠的。