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A Novel Hybrid Method to Predict PM(2.5) Concentration Based on the SWT-QPSO-LSTM Hybrid Model
PM(2.5) concentration is an important indicator to measure air quality. Its value is affected by meteorological factors and air pollutants, so it has the characteristics of nonlinearity, irregularity, and uncertainty. To accurately predict PM(2.5) concentration, this paper proposes a hybrid predicti...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9398715/ https://www.ncbi.nlm.nih.gov/pubmed/36017460 http://dx.doi.org/10.1155/2022/7207477 |
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author | Du, Meng Chen, Yixin Liu, Yang Yin, Hang |
author_facet | Du, Meng Chen, Yixin Liu, Yang Yin, Hang |
author_sort | Du, Meng |
collection | PubMed |
description | PM(2.5) concentration is an important indicator to measure air quality. Its value is affected by meteorological factors and air pollutants, so it has the characteristics of nonlinearity, irregularity, and uncertainty. To accurately predict PM(2.5) concentration, this paper proposes a hybrid prediction system based on the Synchrosqueezing Wavelet Transform (SWT) method, Quantum Particle Swarm Optimization (QPSO) algorithm, and Long Short-Term Memory (LSTM) model. First, the original data are denoised by the SWT method and taken as the input of the prediction model. Then, the main parameters of the LSTM model are optimized by global search based on the QPSO algorithm, which solves the problems of slow convergence and local extremum of traditional parameter training algorithms. Finally, the PM(2.5) daily concentration data of Chengdu, Shijiazhuang, Shenyang, and Wuhan are predicted by the proposed SWT-QPSO-LSTM model, and the prediction results are compared with those of single prediction models and hybrid prediction models. The experimental results show that the proposed model achieves higher prediction precision and lower prediction error than other models. |
format | Online Article Text |
id | pubmed-9398715 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-93987152022-08-24 A Novel Hybrid Method to Predict PM(2.5) Concentration Based on the SWT-QPSO-LSTM Hybrid Model Du, Meng Chen, Yixin Liu, Yang Yin, Hang Comput Intell Neurosci Research Article PM(2.5) concentration is an important indicator to measure air quality. Its value is affected by meteorological factors and air pollutants, so it has the characteristics of nonlinearity, irregularity, and uncertainty. To accurately predict PM(2.5) concentration, this paper proposes a hybrid prediction system based on the Synchrosqueezing Wavelet Transform (SWT) method, Quantum Particle Swarm Optimization (QPSO) algorithm, and Long Short-Term Memory (LSTM) model. First, the original data are denoised by the SWT method and taken as the input of the prediction model. Then, the main parameters of the LSTM model are optimized by global search based on the QPSO algorithm, which solves the problems of slow convergence and local extremum of traditional parameter training algorithms. Finally, the PM(2.5) daily concentration data of Chengdu, Shijiazhuang, Shenyang, and Wuhan are predicted by the proposed SWT-QPSO-LSTM model, and the prediction results are compared with those of single prediction models and hybrid prediction models. The experimental results show that the proposed model achieves higher prediction precision and lower prediction error than other models. Hindawi 2022-08-16 /pmc/articles/PMC9398715/ /pubmed/36017460 http://dx.doi.org/10.1155/2022/7207477 Text en Copyright © 2022 Meng Du et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Du, Meng Chen, Yixin Liu, Yang Yin, Hang A Novel Hybrid Method to Predict PM(2.5) Concentration Based on the SWT-QPSO-LSTM Hybrid Model |
title | A Novel Hybrid Method to Predict PM(2.5) Concentration Based on the SWT-QPSO-LSTM Hybrid Model |
title_full | A Novel Hybrid Method to Predict PM(2.5) Concentration Based on the SWT-QPSO-LSTM Hybrid Model |
title_fullStr | A Novel Hybrid Method to Predict PM(2.5) Concentration Based on the SWT-QPSO-LSTM Hybrid Model |
title_full_unstemmed | A Novel Hybrid Method to Predict PM(2.5) Concentration Based on the SWT-QPSO-LSTM Hybrid Model |
title_short | A Novel Hybrid Method to Predict PM(2.5) Concentration Based on the SWT-QPSO-LSTM Hybrid Model |
title_sort | novel hybrid method to predict pm(2.5) concentration based on the swt-qpso-lstm hybrid model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9398715/ https://www.ncbi.nlm.nih.gov/pubmed/36017460 http://dx.doi.org/10.1155/2022/7207477 |
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