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Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer

Air pollution is a serious problem that affects economic development and people’s health, so an efficient and accurate air quality prediction model would help to manage the air pollution problem. In this paper, we build a combined model to accurately predict the AQI based on real AQI data from four...

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Autores principales: Duan, Jiahui, Gong, Yaping, Luo, Jun, Zhao, Zhiyao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372025/
https://www.ncbi.nlm.nih.gov/pubmed/37495616
http://dx.doi.org/10.1038/s41598-023-36620-4
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author Duan, Jiahui
Gong, Yaping
Luo, Jun
Zhao, Zhiyao
author_facet Duan, Jiahui
Gong, Yaping
Luo, Jun
Zhao, Zhiyao
author_sort Duan, Jiahui
collection PubMed
description Air pollution is a serious problem that affects economic development and people’s health, so an efficient and accurate air quality prediction model would help to manage the air pollution problem. In this paper, we build a combined model to accurately predict the AQI based on real AQI data from four cities. First, we use an ARIMA model to fit the linear part of the data and a CNN-LSTM model to fit the non-linear part of the data to avoid the problem of blinding in the CNN-LSTM hyperparameter setting. Then, to avoid the blinding dilemma in the CNN-LSTM hyperparameter setting, we use the Dung Beetle Optimizer algorithm to find the hyperparameters of the CNN-LSTM model, determine the optimal hyperparameters, and check the accuracy of the model. Finally, we compare the proposed model with nine other widely used models. The experimental results show that the model proposed in this paper outperforms the comparison models in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R(2)). The RMSE values for the four cities were 7.594, 14.94, 7.841 and 5.496; the MAE values were 5.285, 10.839, 5.12 and 3.77; and the R(2) values were 0.989, 0.962, 0.953 and 0.953 respectively.
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spelling pubmed-103720252023-07-28 Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer Duan, Jiahui Gong, Yaping Luo, Jun Zhao, Zhiyao Sci Rep Article Air pollution is a serious problem that affects economic development and people’s health, so an efficient and accurate air quality prediction model would help to manage the air pollution problem. In this paper, we build a combined model to accurately predict the AQI based on real AQI data from four cities. First, we use an ARIMA model to fit the linear part of the data and a CNN-LSTM model to fit the non-linear part of the data to avoid the problem of blinding in the CNN-LSTM hyperparameter setting. Then, to avoid the blinding dilemma in the CNN-LSTM hyperparameter setting, we use the Dung Beetle Optimizer algorithm to find the hyperparameters of the CNN-LSTM model, determine the optimal hyperparameters, and check the accuracy of the model. Finally, we compare the proposed model with nine other widely used models. The experimental results show that the model proposed in this paper outperforms the comparison models in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R(2)). The RMSE values for the four cities were 7.594, 14.94, 7.841 and 5.496; the MAE values were 5.285, 10.839, 5.12 and 3.77; and the R(2) values were 0.989, 0.962, 0.953 and 0.953 respectively. Nature Publishing Group UK 2023-07-26 /pmc/articles/PMC10372025/ /pubmed/37495616 http://dx.doi.org/10.1038/s41598-023-36620-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Duan, Jiahui
Gong, Yaping
Luo, Jun
Zhao, Zhiyao
Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer
title Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer
title_full Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer
title_fullStr Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer
title_full_unstemmed Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer
title_short Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer
title_sort air-quality prediction based on the arima-cnn-lstm combination model optimized by dung beetle optimizer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10372025/
https://www.ncbi.nlm.nih.gov/pubmed/37495616
http://dx.doi.org/10.1038/s41598-023-36620-4
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