Comprehensive Study Conducted on the Potential of Seismic Events in Coal Mining Procedure

Weiwei  Cao; Xiaobin  Zhang; Xingxing  Shen

doi:10.6180/jase.202608_31.039

Comprehensive Study Conducted on the Potential of Seismic Events in Coal Mining Procedure

Weiwei Cao¹, Xiaobin Zhang¹, and Xingxing Shen²This email address is being protected from spambots. You need JavaScript enabled to view it.

¹Tangshan Polytechnic University, Tangshan 063299, China

²Hebei Professional College of Political Science and Law, Shijiazhuang 050000, China

Received: May 15, 2025
Accepted: November 16, 2025
Publication Date: March 1, 2026

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202608_31.039

Accurate prediction of seismic events is critical to enhancing safety and mitigating risks in coal mining operations. This study uses the long-term correlations present in historical seismic data to investigate how well machine learning (ML) techniques forecast seismic activity. Specifically, an Extra Trees Classifier (ETC) is employed and enhanced through integration with two metaheuristic optimization algorithms: Red-Tailed Hawk (RTH) and Bonobo Optimization (BO). These hybrid models, ETRT and ETBO, are developed to improve prediction precision under both hazardous and non-hazardous conditions. Results demonstrate that while the base ETC model achieves high accuracy in hazardous scenarios (precision: 0.990), the hybrid models outperform it with a perfect precision of 1.000 . Under non-hazardous conditions, the ETBO scheme shows improved performance over ETC, achieving a precision of 0.780 compared to 0.540 , though the ETRT model remains the most effective overall (precision: 0.870 ). The findings highlight the potential of hybrid ML-optimization frameworks in seismic risk forecasting and contribute to advancing predictive tools for safer mining operations.

Keywords: Seismic Prediction, Machine Learning, Extra Tree Classification, Red-Tailed Hawk Algorithm, Bonobo Optimization, Coal Mine Safety

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