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

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.