Some reflections on the application of machine learning to research into the theoretical system of mine water prevention and control

Authors

YAO Hui, College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China; Hebei State Key Laboratory of Mine Disaster Prevention, North China Institute of Science and Technology, Beijing 101601, ChinaFollow
YIN Huichao, School of Information Engineering, Institute of Disater Prevention, Langfang 065201, China
LIANG Manyu, Hebei State Key Laboratory of Mine Disaster Prevention, North China Institute of Science and Technology, Beijing 101601, China
YIN Shangxian, Hebei State Key Laboratory of Mine Disaster Prevention, North China Institute of Science and Technology, Beijing 101601, China
HOU Enke, College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
LIAN Huiqing, Hebei State Key Laboratory of Mine Disaster Prevention, North China Institute of Science and Technology, Beijing 101601, ChinaFollow
XIA Xiangxue, Hebei State Key Laboratory of Mine Disaster Prevention, North China Institute of Science and Technology, Beijing 101601, China
ZHANG Jinfu, Shanxi Shuozhou Pinglu District Guoqiang Coal Industry Co., Ltd., Shuozhou 036012, China
WU Chuanshi, Shanxi Shuozhou Pinglu District Guoqiang Coal Industry Co., Ltd., Shuozhou 036012, China

Abstract

The theoretical system of mine water prevention and control encompasses three fundamental aspects: disaster-causing mechanisms, risk evaluation, and disaster prediction. This theoretical system, having undergone rapid development over the past 20 years, aims to gain insights into the behavior characteristics of mine water and predict its evolutionary trend, thus serving the prevention and control of water disasters in mining areas. Applying machine learning, a powerful tool for data analysis and mining in the era of big data, to research into the theoretical system has garnered considerable attention. This study focuses on the specific applications of machine learning to the three fundamental aspects of the theoretical system. Specifically, this study offered a brief introduction to the current status of research on disaster-causing mechanisms based on the classification of varying water disasters, proposing that the application gap of machine learning to the mechanism research is due to its incapacity to make assumptions. This study posited that future research on disaster-causing mechanisms will still primarily rely on conventional methods like theoretical analysis, numerical simulation, and similarity simulation, with machine learning facilitating the acquisition and processing of geologic data. The analysis of method advantages reveals that the application of machine learning to the risk evaluation primarily via processing unstructured data and enriching evaluation methods. For disaster prediction, this study analyzed the drawbacks of prediction modes based merely on physics or data and expounded on the necessity of combining physical models with data-driven approaches. Accordingly, this study presented three methods for achieving the model-data dual-driven prediction mode. Additionally, this study explored the feasibility of image-based disaster prediction methods. With the increasing abundance of production and geologic data, machine learning will accelerate the development of the theoretical system, contributing to research on the systematic methodology for mine water prevention and control.

Keywords

machine learning, mine water inrush, mine water prevention and control, theoretical system, big data

DOI

10.12363/issn.1001-1986.23.10.0641

Recommended Citation

YAO Hui, YIN Huichao, LIANG Manyu, et al. (2024) "Some reflections on the application of machine learning to research into the theoretical system of mine water prevention and control," Coal Geology & Exploration: Vol. 52: Iss. 5, Article 12.
DOI: 10.12363/issn.1001-1986.23.10.0641
Available at: https://cge.researchcommons.org/journal/vol52/iss5/12

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