AdaBoost model for rockburst intensity prediction considering class differences and quantitative characterization of misclassification difference

Yunzhen Zhang; Guangquan Zhang; Tengda Huang; Yuxin Liu; Nanyan Hu

doi:10.1038/s41598-024-79141-4

AdaBoost model for rockburst intensity prediction considering class differences and quantitative characterization of misclassification difference

Sci Rep. 2024 Nov 15;14(1):28232. doi: 10.1038/s41598-024-79141-4.

Authors

Yunzhen Zhang¹, Guangquan Zhang^{2

3}, Tengda Huang¹, Yuxin Liu¹, Nanyan Hu^{1

4}

Affiliations

¹ School of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China.
² School of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China. zhanggq@wust.edu.cn.
³ Hubei Key Laboratory for Efficient Utilization and Agglomeration of Metallurgic Mineral Resource, Wuhan, 430081, Hubei, China. zhanggq@wust.edu.cn.
⁴ Hubei Key Laboratory for Efficient Utilization and Agglomeration of Metallurgic Mineral Resource, Wuhan, 430081, Hubei, China.

Abstract

With the resource development gradually into the deep, rock explosion phenomenon is more and more frequent. The suddenness and harmfulness of rockbursts threaten the safe development of underground resources. In order to more accurately predict the possible intensities of rockbursts in specific rock conditions and stress environments, the rock mechanical and stress parameters between different intensities of rockbursts are further explored, an AdaBoost model considering the differences in the hierarchy is established, and a Flash Hill-Climbing method is proposed to optimize the hyperparameters in the classification model. Finally, a misclassification difference index (MCDI) is defined to quantitatively characterize the severity of misclassification. The results show that the accuracy of the Improved AdaBoost model is about 2.3% higher than that of the Normal AdaBoost model, and the Misclassification Difference Index (MCDI) of the Normal AdaBoost model is 05, while the the Improved AdaBoost model is 00. The model can provide a theoretical reference for rockburst prediction.

Keywords: Flash Hill-climbing method; Improved Adaboost model; Misclassification difference index; Rockburst intensity prediction.

Abstract

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