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Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series

Author

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  • Tianjun Zhang

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

  • Shuang Song

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

  • Shugang Li

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

  • Li Ma

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

  • Shaobo Pan

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

  • Liyun Han

    (College of Humanities and International Education, Xi’an Peihua University, Xi’an 710125, China)

Abstract

Effective prediction of gas concentrations and reasonable development of corresponding safety measures have important guiding significance for improving coal mine safety management. In order to improve the accuracy of gas concentration prediction and enhance the applicability of the model, this paper proposes a long short-term memory (LSTM) cyclic neural network prediction method based on actual coal mine production monitoring data to select gas concentration time series with larger samples and longer time spans, including model structural design, model training, model prediction, and model optimization to implement the prediction algorithm. By using the minimum objective function as the optimization goal, the Adam optimization algorithm is used to continuously update the weight of the neural network, and the network layer and batch size are tuned to select the optimal one. The number of layers and batch size are used as parameters of the coal mine gas concentration prediction model. Finally, the optimized LSTM prediction model is called to predict the gas concentration in the next time period. The experiment proves the following: The LSTM gas concentration prediction model uses large data volume sample prediction, more accurate than the bidirectional recurrent neural network (BidirectionRNN) model and the gated recurrent unit (GRU) model. The average mean square error of the prediction model can be reduced to 0.003 and the predicted mean square error can be reduced to 0.015, which has higher reliability in gas concentration time series prediction. The prediction error range is 0.0005–0.04, which has better robustness in gas concentration time series prediction. When predicting the trend of gas concentration time series, the gas concentration at the time inflection point can be better predicted and the mean square error at the inflection point can be reduced to 0.014, which has higher applicability in gas concentration time series prediction.

Suggested Citation

  • Tianjun Zhang & Shuang Song & Shugang Li & Li Ma & Shaobo Pan & Liyun Han, 2019. "Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series," Energies, MDPI, vol. 12(1), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:1:p:161-:d:194755
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    Citations

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    Cited by:

    1. Wen, Hu & Yan, Li & Jin, Yongfei & Wang, Zhipeng & Guo, Jun & Deng, Jun, 2023. "Coalbed methane concentration prediction and early-warning in fully mechanized mining face based on deep learning," Energy, Elsevier, vol. 264(C).
    2. Xiangrui Meng & Haoqian Chang & Xiangqian Wang, 2022. "Methane Concentration Prediction Method Based on Deep Learning and Classical Time Series Analysis," Energies, MDPI, vol. 15(6), pages 1-15, March.
    3. Maksymilian Mądziel, 2023. "Future Cities Carbon Emission Models: Hybrid Vehicle Emission Modelling for Low-Emission Zones," Energies, MDPI, vol. 16(19), pages 1-16, October.
    4. Andre S. Barcelos & Antonio J. Marques Cardoso, 2021. "Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms," Energies, MDPI, vol. 14(9), pages 1-14, April.
    5. Yuxin Huang & Jingdao Fan & Zhenguo Yan & Shugang Li & Yanping Wang, 2022. "A Gas Concentration Prediction Method Driven by a Spark Streaming Framework," Energies, MDPI, vol. 15(15), pages 1-13, July.
    6. Souradeep Chakraborty, 2019. "Capturing Financial markets to apply Deep Reinforcement Learning," Papers 1907.04373, arXiv.org, revised Dec 2019.
    7. Fei Qian & Li Chen & Jun Li & Chao Ding & Xianfu Chen & Jian Wang, 2019. "Direct Prediction of the Toxic Gas Diffusion Rule in a Real Environment Based on LSTM," IJERPH, MDPI, vol. 16(12), pages 1-14, June.
    8. Shuang Song & Shugang Li & Tianjun Zhang & Li Ma & Shaobo Pan & Lu Gao, 2021. "Research on a Multi-Parameter Fusion Prediction Model of Pressure Relief Gas Concentration Based on RNN," Energies, MDPI, vol. 14(5), pages 1-18, March.

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