Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model

In this study, to further improve the prediction accuracy of coal mine gas concentration and thereby preventing gas accidents and improving coal mine safety management, the standard whale optimisation algorithm’s (WOA) susceptibility to falling into local optima, slow convergence speed, and low pred...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 22; no. 12; p. 4412
Main Authors: Xu, Ningke, Wang, Xiangqian, Meng, Xiangrui, Chang, Haoqian
Format: Journal Article
Language:English
Published: Basel MDPI AG 10.06.2022
MDPI
Subjects:
Accuracy
Algorithms
CEEMDAN decomposition and reconstruction
coal mine safety
Coal mining
Data processing
Datasets
Decomposition
Deep learning
gas concentration prediction
Geology
LSTM
Machine learning
Mathematical models
Mines
Neural networks
Trends
whale optimisation algorithm
China
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:In this study, to further improve the prediction accuracy of coal mine gas concentration and thereby preventing gas accidents and improving coal mine safety management, the standard whale optimisation algorithm’s (WOA) susceptibility to falling into local optima, slow convergence speed, and low prediction accuracy of the single-factor long short-term memory (LSTM) neural network residual correction model are addressed. A new IWOA-LSTM-CEEMDAN model is constructed based on the improved whale optimisation algorithm (IWOA) to improve the IWOA-LSTM one-factor residual correction model through the use of the complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) method. The population diversity of the WOA is enhanced through multiple strategies and its ability to exit local optima and perform global search is improved. In addition, the optimal weight combination model for subsequence is determined by analysing the prediction error of the intrinsic mode function (IMF) of the residual sequence. The experimental results show that the prediction accuracy of the IWOA-LSTM-CEEMDAN model is higher than that of the BP neural network and the GRU, LSTM, WOA-LSTM, and IWOA-LSTM residual correction models by 47.48%, 36.48%, 30.71%, 27.38%, and 12.96%, respectively. The IWOA-LSTM-CEEMDAN model also achieves the highest prediction accuracy in multi-step prediction.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22124412