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LSTM-Pearson Gas Concentration Prediction Model Feature Selection and Its Applications

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  • Chao Liu

    (College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
    Key Laboratory of Western Mine Exploitation and Hazard Prevention of the Ministry of Education, Xi’an 710054, China)

  • Ailin Zhang

    (College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Junhua Xue

    (College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
    Key Laboratory of Western Mine Exploitation and Hazard Prevention of the Ministry of Education, Xi’an 710054, China)

  • Chen Lei

    (College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Xiangzhen Zeng

    (College of Safety Science and Engineering, Xi’an University of Science and Technology, Xi’an 710054, China)

Abstract

Gas disasters threaten the safe operation of coal mines. Improving the accuracy of gas concentration predictions can effectively prevent gas disasters and reduce disaster losses. Traditional gas concentration prediction methods poorly couple the gas concentration and its influencing factors when dealing with a great number of features and data, which results in low prediction accuracy and poor efficiency. To solve this problem, we used an innovative Pearson-LSTM prediction model, which uses the Pearson coefficient to select features of gas concentration data. It then uses long short-term memory (LSTM) that has been optimized using adaptive moment estimation (Adam) to predict a time series. In the process of model establishment, the optimal prediction model was obtained by constantly adjusting the number of network layers and batch size based on the fitting effect, performance issues, and result errors. Taking monitoring data from the 2407 working face at Yuhua Coal Mine as the sample, we compared our method with the traditional Bi-RNN and GRU machine learning methods. The results show that, compared with the Bi-RNN and GRU models, the mean square error of the Pearson-LSTM model can be reduced to 0.015 with an error range of 0.005 to 0.04, which has higher prediction accuracy. This method has excellent precision and robustness for forecasting gas concentration time series. The model was able to make predictions 15 min in advance for the 2409 working face of the Yuhua Coal Mine, and the mean square error could be lowered to 0.008, which verifies the applicability and reliability of the model and provides a reference for ensuring the safety of coal mine operations. In summary, Pearson-LSTM models have higher accuracy and robustness and can effectively predict changes in gas concentration, thus allowing for more response time for accidents, which is important for coal mine production safety.

Suggested Citation

  • Chao Liu & Ailin Zhang & Junhua Xue & Chen Lei & Xiangzhen Zeng, 2023. "LSTM-Pearson Gas Concentration Prediction Model Feature Selection and Its Applications," Energies, MDPI, vol. 16(5), pages 1-16, February.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2318-:d:1083197
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    References listed on IDEAS

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    1. Hui Song & Jiejie Dai & Lingen Luo & Gehao Sheng & Xiuchen Jiang, 2018. "Power Transformer Operating State Prediction Method Based on an LSTM Network," Energies, MDPI, vol. 11(4), pages 1-15, April.
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