Prediction of Gas Concentration Based on LSTM-LightGBM Variable Weight Combination Model

Gas accidents threaten the safety of underground coal mining, which are always accompanied by abnormal gas concentration trend. The purpose of this paper is to improve the prediction accuracy of gas concentration so as to prevent gas accidents and improve the level of coal mine safety management. Co...

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Bibliographic Details
Published in:Energies (Basel) Vol. 15; no. 3; p. 827
Main Authors: Wang, Xiangqian, Xu, Ningke, Meng, Xiangrui, Chang, Haoqian
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.02.2022
Subjects:
Accuracy
Algorithms
coal mine safety
Coal mining
Correlation analysis
Data collection
Datasets
Feature selection
gas concentration prediction
LightGBM
LSTM
LSTM-LightGBM variable weight combination
Machine learning
Neural networks
Time series
China
ISSN:1996-1073, 1996-1073
Online Access:Get full text
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Summary:Gas accidents threaten the safety of underground coal mining, which are always accompanied by abnormal gas concentration trend. The purpose of this paper is to improve the prediction accuracy of gas concentration so as to prevent gas accidents and improve the level of coal mine safety management. Combining the LSTM model with the LightGBM model, the LSTM-LightGBM model is proposed with variable weight combination method based on residual assignment, which considers not only the time subsequence feature of data, but also the nonlinear characteristics of data. During the data preprocessing, the optimal parameters of gas concentration prediction are determined through the analysis of the Pearson correlation coefficients of different sensor data. The experimental results demonstrate that the mean absolute errors of LSTM-LighGBM, LSTM and LightGBM are 1.94%, 2.19% and 2.77%, respectively. The accuracy of LSTM-LightGBM variable weight combination model is better than that of the two above models, respectively. In this way, this study provides a novel idea and method for gas accident prevention based on gas concentration prediction.
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content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en15030827