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Evaluation of deep coal and gas outburst based on RS-GA-BP

Author

Listed:
  • Junqi Zhu

    (Anhui University of Science and Technology)

  • Haotian Zheng

    (Anhui University of Science and Technology)

  • Li Yang

    (Anhui University of Science and Technology)

  • Shanshan Li

    (Anhui University of Science and Technology)

  • Liyan Sun

    (Anhui University of Science and Technology)

  • Jichao Geng

    (Anhui University of Science and Technology)

Abstract

Owing to the high dimension and nonlinear characteristics of gas outbursts in deep coal mines, an intelligent evaluation method for systematically screening and integrating gas data in deep coal mines is proposed herein to effectively identify coal and gas outbursts in deep mines. A rough set improved using a genetic algorithm is introduced to reduce the dimension of complex data pertaining to deep coal mine gas to determine the main control index of deep coal and gas outbursts. Subsequently, the initial weight and threshold of a back propagation (BP) neural network are optimized by combining the characteristics of parallelism and robustness of the genetic algorithm. An adaptive optimization of BP neural network by genetic algorithm back propagation (GA-BP) model is established to identify gas outburst in deep coal mine reasonably. Compared with the standard BP neural network, data fitting shows that the method can significantly improve the detection accuracy of deep coal and gas outburst while improving the speed of disaster identification, as well as improve the efficiency of disaster identification, thereby increasing the risk identification accuracy of deep coal and gas outburst to 90%. This not only provides a new method for the scientific evaluation of deep coal and gas outburst risk, but also an important reference for the scientific evaluation of other high-dimensional and nonlinear fields.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:nathaz:v:115:y:2023:i:3:d:10.1007_s11069-022-05652-w
    DOI: 10.1007/s11069-022-05652-w
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    References listed on IDEAS

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    1. Zhou Tao & Lu Huiling & Fuyuan Hu & Shi Qiu & Wu Cuiying, 2020. "A Model of High-Dimensional Feature Reduction Based on Variable Precision Rough Set and Genetic Algorithm in Medical Image," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-18, May.
    2. 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.
    3. Guo-Ying Wei & Fang-Chao Kang & Bin-Bin Qin & Tian-Rang Jia & Jiang-Wei Yan & Zhen-Dong Feng, 2020. "A novel method for evaluating proneness of gas outburst based on gas-geological complexity," 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. 104(2), pages 1841-1858, November.
    4. Junhong Si & Lin Li & Jianwei Cheng & Yiqiao Wang & Wei Hu & Tan Li & Zequan Li, 2021. "Characteristics of Airflow Reversal of Excavation Roadway after a Coal and Gas Outburst Accident," Energies, MDPI, vol. 14(12), pages 1-13, June.
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