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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9230321/ https://www.ncbi.nlm.nih.gov/pubmed/35746193 http://dx.doi.org/10.3390/s22124412 |
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author | Xu, Ningke Wang, Xiangqian Meng, Xiangrui Chang, Haoqian |
author_facet | Xu, Ningke Wang, Xiangqian Meng, Xiangrui Chang, Haoqian |
author_sort | Xu, Ningke |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9230321 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92303212022-06-25 Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model Xu, Ningke Wang, Xiangqian Meng, Xiangrui Chang, Haoqian Sensors (Basel) Article 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. MDPI 2022-06-10 /pmc/articles/PMC9230321/ /pubmed/35746193 http://dx.doi.org/10.3390/s22124412 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xu, Ningke Wang, Xiangqian Meng, Xiangrui Chang, Haoqian Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model |
title | Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model |
title_full | Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model |
title_fullStr | Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model |
title_full_unstemmed | Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model |
title_short | Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model |
title_sort | gas concentration prediction based on iwoa-lstm-ceemdan residual correction model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9230321/ https://www.ncbi.nlm.nih.gov/pubmed/35746193 http://dx.doi.org/10.3390/s22124412 |
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