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Study on the Prediction of Low-Index Coal and Gas Outburst Based on PSO-SVM

Author

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  • Yunpei Liang

    (State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
    School of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China)

  • Shuren Mao

    (State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
    School of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China)

  • Menghao Zheng

    (State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
    School of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China)

  • Quangui Li

    (State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
    School of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China)

  • Xiaoyu Li

    (State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
    School of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China)

  • Jianbo Li

    (State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
    School of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China)

  • Junjiang Zhou

    (State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China
    School of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China)

Abstract

Low-index coal and gas outburst (LI-CGO) is difficult to predict, which seriously threatens the efficient mining of coal. To predict the LI-CGO, the Support Vector Machine (SVM) algorithm was used in this study. The Particle Swarm Optimization (PSO) algorithm was used to optimize the parameters of the SVM algorithm. The results show that based on the training sets and test set in this study, the prediction accuracy of SVM is higher than that of Back Propagation Neural Network and Distance Discriminant Analysis. The prediction accuracy of the SVM model trained by the training set T2 with LI-CGO cases is higher than that of the SVM model trained by the training set T1 without LI-CGO cases. The prediction accuracy gets better when the SVM model is trained by the training set T3, made by adding the data of the other two coal mines (EH and SH) to the training set T2, that only contains the data of XP and PJ. Furthermore, the PSO-SVM model achieves a better predictive effect than the SVM model, with an accuracy rate of 90%. The research results can provide a method reference for the prediction of LI-CGO.

Suggested Citation

  • Yunpei Liang & Shuren Mao & Menghao Zheng & Quangui Li & Xiaoyu Li & Jianbo Li & Junjiang Zhou, 2023. "Study on the Prediction of Low-Index Coal and Gas Outburst Based on PSO-SVM," Energies, MDPI, vol. 16(16), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:5990-:d:1217908
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    References listed on IDEAS

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    1. Babacar Gaye & Dezheng Zhang & Aziguli Wulamu, 2021. "Improvement of Support Vector Machine Algorithm in Big Data Background," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-9, June.
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