Methane Concentration Prediction Method Based on Deep Learning and Classical Time Series Analysis

Xiangrui Meng, Haoqian Chang and Xiangqian Wang
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Xiangrui Meng: School of Economics and Management, Anhui University of Science & Technology, Huainan 232000, China
Haoqian Chang: School of Economics and Management, Anhui University of Science & Technology, Huainan 232000, China
Xiangqian Wang: School of Economics and Management, Anhui University of Science & Technology, Huainan 232000, China

Energies, 2022, vol. 15, issue 6, 1-15

Abstract: Methane is one of the most dangerous gases encountered in the mining industry. During mining operations, methane can be broadly classified into three states: mining excavation, stoppage safety check, and abnormal methane concentration, which is usually a precursor to a gas accident, such as a coal and gas outburst. Consequently, it is vital to accurately predict methane concentrations. Herein, we apply three deep learning methods—a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU)—to the problem of methane concentration prediction and evaluate their efficacy. In addition, we propose a novel prediction method that combines classical time series analysis with these deep learning models. The results revealed that GRU has the least root mean square error (RMSE) loss of the three models. The RMSE loss can be further reduced by approximately 35% by using the proposed combined approach, and the models are also less likely to result in overfitting. Therefore, combining deep learning methods with classical time series analysis can provide accurate methane concentration prediction and improve mining safety.

Keywords: methane concentration prediction; mining safety; deep learning; time series analysis; recurrent neural network (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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