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

Tianjun Zhang, Shuang Song, Shugang Li, Li Ma, Shaobo Pan and Liyun Han
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Tianjun Zhang: College of Science, 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

Energies, 2019, vol. 12, issue 1, 1-15

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.

Keywords: coal mine safety; recurrent neural network; deep learning; LSTM regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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