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Real-Time Prediction Model of Coal and Gas Outburst

Author

Listed:
  • Ru Yandong
  • Lv Xingfeng
  • Guo Jikun
  • Zhang Hongquan
  • Chen Lijuan

Abstract

Coal and gas outburst has been one of the main threats to coal mine safety. Accurate coal and gas outburst prediction is the key to avoid accidents. The data is actual and complete by default in the existing prediction model. However, in fact, data missing and abnormal data value often occur, which results in poor prediction performance. Therefore, this paper proposes to use the correlation coefficient to complete the missing data filling in real time for the first time. The abnormal data identification is completed based on the Pauta criterion. Random forest model is used to realize the prediction model. The prediction performance of sensitivity 100%, accuracy 97.5%, and specificity 84.6% were obtained. Experiments show that the model can complete the prediction of coal and gas outburst in real time under the condition of missing data and abnormal data value, which can be used as a new prediction model of coal and gas outburst.

Suggested Citation

  • Ru Yandong & Lv Xingfeng & Guo Jikun & Zhang Hongquan & Chen Lijuan, 2020. "Real-Time Prediction Model of Coal and Gas Outburst," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-5, October.
    • Handle: RePEc:hin:jnlmpe:2432806
    DOI: 10.1155/2020/2432806
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