Intelligent classification with class imbalance for surrounding rock squeezing in underground engineering

Surrounding rock squeezing, as a typical geological disaster, occurs frequently during underground excava-tion. To accurately and quickly predict squeezing risk, machine learning is introduced, which has strong abilities to deal with the nonlinear relationship between surrounding rock squeezing and multiple disaster-triggering factors. How-ever, the prediction performance of machine learning is greatly affected by the quality of the data. Consequently, this study takes into account class imbalance and proposes a novel intelligent paradigm for squeezing prediction. First, at the data level, synthetic minority oversampling technique (SMOTE) is used to create a more balanced data environment. And then, to increase the utilization rate of the minority, ensemble learning (EL) is used to mine data value and build prediction models, including extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), ran-dom forest (RF), and extremely randomized tree (ERT). And at the same time, whale optimization algorithm (WOA) is integrated to optimize the hyperparameters of SMOTE and EL. The results reveal that the model of SMOTE-ERT performs best. Its accuracy and macro F1-score reach 93.94% and 0.9323 respectively. Additionally, through the com-parative analysis, the necessity of SMOTE is affirmed. Finally, taking the optimal model as an example, the contribu-tion of each input parameter is quantitatively evaluated, and strength-stress ratio plays the most important role in squeezing prediction.