Time Series Prediction of Gas Emission in Coal Mining Face Based on Optimized Variational Mode Decomposition and SSA-LSTM

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 19; p. 6454
• Main Authors: Zhang, Jingzhao, Cui, Yuxin, Yan, Zhenguo, Huang, Yuxin, Zhang, Chenyu, Zhang, Jinlong, Guo, Jiantao, Zhao, Fei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 06-10-2024; MDPI
• Subjects: Accuracy; Algorithms; Analysis; Coal; Coal industry; Deep learning; Emissions (Pollution); Entropy; Forecasts and trends; Genetic algorithms; Lagrange multiplier; long short-term memory; Methods; Mines and mineral resources; Optimization; sparrow search algorithm; Spectrum analysis; Time series; time series prediction of gas emissions; variational mode decomposition; sparrow search algorithm; variational mode decomposition; long short-term memory; time series prediction of gas emissions
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24196454

The accurate prediction of gas emissions has important guiding significance for the prevention and control of gas disasters in order to further improve the prediction accuracy of gas emissions in the mining face. According to the absolute gas emission monitoring data of the 1417 working face in a coal mine in Shaanxi Province, a GA-VMD-SSA-LSTM gas emission prediction model (GVSL) based on genetic algorithm (GA)-optimized variational mode decomposition (VMD) and sparrow search algorithm (SSA)-optimized long short-term memory (LSTM) is proposed. Firstly, a VMD evaluation standard for evaluating the amount of decomposition loss is proposed. Under this standard, the GA is used to find the optimal parameters of the VMD. Then, the SSA is used to optimize the key parameters of the LSTM to establish a GVSL prediction model. The model predicts each component and finally superimposes the prediction results for each component to obtain the final gas emission result. The results show that the accuracy of the evaluation indexes of the GVSL model and VMD-LSTM model, as well as the SSA-LSTM model and Gaussian process regression (GPR) model, are compared and analyzed horizontally and vertically under three scenarios with prediction sets of 121,94 and 57 groups. The GVSL model has the best prediction effect, and its fitting degree R2 values are 0.95, 0.96, and 0.99, which confirms the effectiveness of the proposed GVSL model for the time series prediction of gas emission in the mining face.