Application of KM-SMOTE for rockburst intelligent prediction: Fid-Bau Portal

Application of KM-SMOTE for rockburst intelligent prediction

Liu, Qiushi / Xue, Yiguo / Li, Guangkun / Qiu, Daohong / Zhang, Weimeng / Guo, Zhuangzhuang / Li, Zhiqiang

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Highlights • KM-SMOTE is expected to reduce class-imbalanced and improve the identification accuracy of minority-class samples. • KM-SMOTE produces fewer noise samples and abnormal points than SMOTE. • KM-SMOTE outperformed SMOTE in terms of predicting rockburst intensity by 6 machine-learning algorithms. • Wet is the most influential index in rockburst prediction due to analysis with CART.

Abstract Class-imbalanced is a common phenomenon in rockburst data, and the prediction of rockburst intensity through intelligent methods requires a balanced dataset. This fact presents challenges for standard classification algorithms that are designed for class distributions that are well-balanced. This paper develops the modified synthetic minority oversampling technique by K-means cluster (KM-SMOTE) to reduce the imbalance phenomenon in the rockburst dataset. First, the study collects 226 rockburst cases worldwide as the original supporting dataset and selects four indexes to predict the rockburst intensity, namely, the maximum tangential stress of the surrounding rock σθ, the uniaxial compressive strength of rock σc, the tensile strength of rock σt, and the elastic energy index Wet. Second, the KM-SMOTE uses a K-means cluster to cluster the minority-class samples and then performs SMOTE oversampling on each cluster to obtain 388 data. To establish a nonlinear correlation between rockburst intensity and its predictors, six machine-learning classifiers are used. The dataset is randomly divided into training and test sets, with 80% of the data used for training. In the data training and testing phases, the original dataset, SMOTE-processed dataset, and KM-SMOTE-processed dataset were put into the machine learning models for predicting rockburst intensity, where KM-SMOTE was 3.3% and 10.5% more accurate than the SMOTE-processed dataset in predicting rockburst intensity, respectively. In the Jiangbian Hydropower Station engineering application, the KM-SMOTE algorithm can achieve a maximum improvement of 25% in accuracy compared with the data processed by SMOTE. Overall, the proposed modified oversampling algorithm effectively overcomes class-imbalanced in the rockburst dataset and significantly contributes to the intelligent prediction of rockburst by machine learning in engineering.