Probabilistic Prediction of Mine Dynamic Disaster Risk Based on Multiple Factor Pattern Recognition

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Abstract
Rock burst and coal and gas outburst are the most serious dynamic disasters in coal mine and are affected by many factors, such as mining engineering environment. In order to accurately predict the risk area of mine dynamic disasters, a series of impact factors and events are classified, and the spatial data of these factors are managed on the basis of identifying the internal relationship between the impact factors and the disasters. A multifactor pattern recognition model is established by artificial intelligence. The risk probability prediction criteria of mine dynamic disasters and the risk probability values of each unit in the prediction area are determined by using the method of neural network and fuzzy mathematics. The dangerous area, threat area, and safety area of mine dynamic disasters are divided to evaluate the dangerous degree. The corresponding control measures for different dangerous areas are also put forward. Application of the prediction method of mine dynamic disaster factors based on pattern recognition, to improve the implementation of mine dynamic disaster prediction and controlling measures, guarantees the safe production of the coal mine.
Funding Information
  • National Natural Science Foundation of China (51674132, 51604139)
  • China University of Mining and Technology (SKLCRSM18KF000)
  • Henan Polytechnic University (WS2018B05)
  • Department of Education of Liaoning Province (LJYL037, LJZS004)

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