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Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM

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  • Haibo Liu
  • Yujie Dong
  • Fuzhong Wang

Abstract

This paper investigates the problem of gas outburst prediction in the working face of coal mine. Firstly, based on a comprehensive analysis of influence factors of gas outburst, an improved entropy weight algorithm is introduced into a grey correlation analysis algorithm; thus, the reasonable weights and correlation order of the influencing factors are obtained to improve the objectivity of the evaluation. The main controlling factors obtained are used as the input of the prediction model. Secondly, by utilizing the improved particle swarm optimization (IPSO), the penalty factor and kernel parameter of least square support vector machine (LSSVM) are optimized to enhance the global search ability and avoid the occurrence of the local optimal solutions, and a new prediction model of gas outburst based on IPSO-LSSVM is established. At last, the prediction model is applied in the tunneling heading face 14141 of Jiuli Hill mine in Jiaozuo City, China. The case study demonstrates that the prediction accuracy of the proposed model is 92%, which is improved compared with that of the SVM model and GA-LSSVM model.

Suggested Citation

  • Haibo Liu & Yujie Dong & Fuzhong Wang, 2020. "Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, November.
    • Handle: RePEc:hin:jnlmpe:8863425
    DOI: 10.1155/2020/8863425
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    Cited by:

    1. Junqi Zhu & Haotian Zheng & Li Yang & Shanshan Li & Liyan Sun & Jichao Geng, 2023. "Evaluation of deep coal and gas outburst based on RS-GA-BP," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(3), pages 2531-2551, February.
    2. Yuan Huang & Junhao Yu & Xiaohong Dai & Zheng Huang & Yuanyuan Li, 2022. "Air-Quality Prediction Based on the EMD–IPSO–LSTM Combination Model," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
    3. Pei Yin & Jing Cheng & Miaojuan Peng, 2022. "Analyzing the Passenger Flow of Urban Rail Transit Stations by Using Entropy Weight-Grey Correlation Model: A Case Study of Shanghai in China," Mathematics, MDPI, vol. 10(19), pages 1-23, September.
    4. Li Yang & Xin Fang & Xue Wang & Shanshan Li & Junqi Zhu, 2022. "Risk Prediction of Coal and Gas Outburst in Deep Coal Mines Based on the SAPSO-ELM Algorithm," IJERPH, MDPI, vol. 19(19), pages 1-18, September.

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