LSTM-based air quality predicted model for large cities in China

Zhang, S; Lin, M; Zou, X; Su, S; Zhang, W; Zhang, X; Guo, Z

LSTM-based air quality predicted model for large cities in China

Zhang, S Lin, M Zou, X Su, S

Zhang, W

Zhang, X Guo, Z

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Publication Type:: Journal Article
Citation:: Nature Environment and Pollution Technology, 2020, 19, (1), pp. 229-236
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Zhang, S
dc.contributor.author	Lin, M
dc.contributor.author	Zou, X
dc.contributor.author	Su, S https://orcid.org/0000-0002-5720-8852
dc.contributor.author	Zhang, W https://orcid.org/0000-0002-2064-0326
dc.contributor.author	Zhang, X
dc.contributor.author	Guo, Z
dc.date.accessioned	2020-09-26T02:23:13Z
dc.date.available	2020-09-26T02:23:13Z
dc.date.issued	2020-01-01
dc.identifier.citation	Nature Environment and Pollution Technology, 2020, 19, (1), pp. 229-236
dc.identifier.issn	0972-6268
dc.identifier.issn	2395-3454
dc.identifier.uri	http://hdl.handle.net/10453/142876
dc.description.abstract	© 2020 Technoscience Publications. All rights reserved. In this paper, the LSTM model is used to predict the PM2.5 concentrations in five representative Chinese cities with the GDP exceeding 1 trillion Yuan, including Beijing, Chengdu, Shanghai, Shenzhen and Wuhan. The PM2.5 concentration data in 2015-2017 are selected for training, and the results are optimized to achieve an efficient solution by adjusting the parameters. Based on the optimized solution, a test is carried out to predict the PM2.5 concentration in 2018, and the results are compared with the real value obtained from the monitoring centre. According to the comparison results, the correlation coefficient of Wuhan and Chengdu is 0.86724 and 0.80070, which are the highest in these five cities. While the correlation coefficient of Shenzhen and Shanghai, are 0.78225, 0.72147, Beijing, as the capital city of China achieved the lowest correlation coefficient which is 0.64118. The LSTM-based predictive model has relatively good reliability and transferability. More effective predictive results can be achieved by implementing deep learning to analyse PM2.5 concentration.
dc.language	en
dc.relation	Commonwealth Scientific and Industrial Research Organisation
dc.relation.ispartof	Nature Environment and Pollution Technology
dc.rights	info:eu-repo/semantics/openAccess
dc.title	LSTM-based air quality predicted model for large cities in China
dc.type	Journal Article
utslib.citation.volume	19
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access	*
dc.date.updated	2020-09-26T02:23:10Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	19
utslib.citation.issue	1

Abstract:

© 2020 Technoscience Publications. All rights reserved. In this paper, the LSTM model is used to predict the PM2.5 concentrations in five representative Chinese cities with the GDP exceeding 1 trillion Yuan, including Beijing, Chengdu, Shanghai, Shenzhen and Wuhan. The PM2.5 concentration data in 2015-2017 are selected for training, and the results are optimized to achieve an efficient solution by adjusting the parameters. Based on the optimized solution, a test is carried out to predict the PM2.5 concentration in 2018, and the results are compared with the real value obtained from the monitoring centre. According to the comparison results, the correlation coefficient of Wuhan and Chengdu is 0.86724 and 0.80070, which are the highest in these five cities. While the correlation coefficient of Shenzhen and Shanghai, are 0.78225, 0.72147, Beijing, as the capital city of China achieved the lowest correlation coefficient which is 0.64118. The LSTM-based predictive model has relatively good reliability and transferability. More effective predictive results can be achieved by implementing deep learning to analyse PM2.5 concentration.

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http://hdl.handle.net/10453/142876