A Haze Prediction Model in Chengdu Based on LSTM

Air pollution with fluidity can influence a large area for a long time and can be harmful to the ecological environment and human health. Haze, one form of air pollution, has been a critical problem since the industrial revolution. Though the actual cause of haze could be various and complicated, in...

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Autores principales:	Xinyi Wu, Zhixin Liu, Lirong Yin, Wenfeng Zheng, Lihong Song, Jiawei Tian, Bo Yang, Shan Liu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	haze prediction multilayer long short-term memory PM2.5 PM10 Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/101f9442d48748438027f90cc94f7f56
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id	oai:doaj.org-article:101f9442d48748438027f90cc94f7f56
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spelling	oai:doaj.org-article:101f9442d48748438027f90cc94f7f562021-11-25T16:45:19ZA Haze Prediction Model in Chengdu Based on LSTM10.3390/atmos121114792073-4433https://doaj.org/article/101f9442d48748438027f90cc94f7f562021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4433/12/11/1479https://doaj.org/toc/2073-4433Air pollution with fluidity can influence a large area for a long time and can be harmful to the ecological environment and human health. Haze, one form of air pollution, has been a critical problem since the industrial revolution. Though the actual cause of haze could be various and complicated, in this paper, we have found out that many gases’ distributions and wind power or temperature are related to PM2.5/10’s concentration. Thus, based on the correlation between PM2.5/PM10 and other gaseous pollutants and the timing continuity of PM2.5/PM10, we propose a multilayer long short-term memory haze prediction model. This model utilizes the concentration of O<sub>3</sub>, CO, NO<sub>2</sub>, SO<sub>2</sub>, and PM2.5/PM10 in the last 24 h as inputs to predict PM2.5/PM10 concentrations in the future. Besides pre-processing the data, the primary approach to boost the prediction performance is adding layers above a single-layer long short-term memory model. Moreover, it is proved that by doing so, we could let the network make predictions more accurately and efficiently. Furthermore, by comparison, in general, we have obtained a more accurate prediction.Xinyi WuZhixin LiuLirong YinWenfeng ZhengLihong SongJiawei TianBo YangShan LiuMDPI AGarticlehaze predictionmultilayer long short-term memoryPM2.5PM10Meteorology. ClimatologyQC851-999ENAtmosphere, Vol 12, Iss 1479, p 1479 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	haze prediction multilayer long short-term memory PM2.5 PM10 Meteorology. Climatology QC851-999
spellingShingle	haze prediction multilayer long short-term memory PM2.5 PM10 Meteorology. Climatology QC851-999 Xinyi Wu Zhixin Liu Lirong Yin Wenfeng Zheng Lihong Song Jiawei Tian Bo Yang Shan Liu A Haze Prediction Model in Chengdu Based on LSTM
description	Air pollution with fluidity can influence a large area for a long time and can be harmful to the ecological environment and human health. Haze, one form of air pollution, has been a critical problem since the industrial revolution. Though the actual cause of haze could be various and complicated, in this paper, we have found out that many gases’ distributions and wind power or temperature are related to PM2.5/10’s concentration. Thus, based on the correlation between PM2.5/PM10 and other gaseous pollutants and the timing continuity of PM2.5/PM10, we propose a multilayer long short-term memory haze prediction model. This model utilizes the concentration of O<sub>3</sub>, CO, NO<sub>2</sub>, SO<sub>2</sub>, and PM2.5/PM10 in the last 24 h as inputs to predict PM2.5/PM10 concentrations in the future. Besides pre-processing the data, the primary approach to boost the prediction performance is adding layers above a single-layer long short-term memory model. Moreover, it is proved that by doing so, we could let the network make predictions more accurately and efficiently. Furthermore, by comparison, in general, we have obtained a more accurate prediction.
format	article
author	Xinyi Wu Zhixin Liu Lirong Yin Wenfeng Zheng Lihong Song Jiawei Tian Bo Yang Shan Liu
author_facet	Xinyi Wu Zhixin Liu Lirong Yin Wenfeng Zheng Lihong Song Jiawei Tian Bo Yang Shan Liu
author_sort	Xinyi Wu
title	A Haze Prediction Model in Chengdu Based on LSTM
title_short	A Haze Prediction Model in Chengdu Based on LSTM
title_full	A Haze Prediction Model in Chengdu Based on LSTM
title_fullStr	A Haze Prediction Model in Chengdu Based on LSTM
title_full_unstemmed	A Haze Prediction Model in Chengdu Based on LSTM
title_sort	haze prediction model in chengdu based on lstm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/101f9442d48748438027f90cc94f7f56
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A Haze Prediction Model in Chengdu Based on LSTM

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