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 develop-ment over the past 20 years,aims to gain insights into the behavior characteristics of mine water and predict its evolu-tionary 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 con-siderable 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 ma-chine learning to the mechanism research is due to its incapacity to make assumptions.This study posited that future re-search on disaster-causing mechanisms will still primarily rely on conventional methods like theoretical analysis,numer-ical 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 draw-backs of prediction modes based merely on physics or data and expounded on the necessity of combining physical mod-els with data-driven approaches.Accordingly,this study presented three methods for achieving the model-data dual-driv-en 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 the-oretical system,contributing to research on the systematic methodology for mine water prevention and control.
machine learningmine water inrushmine water prevention and controltheoretical systembig data