A novel Encoder-Decoder model based on read-first LSTM for air pollutant prediction

Accurate air pollutant prediction allows effective environment management to reduce the impact of pollution and prevent pollution incidents. Existing studies of air pollutant prediction are mostly interdisciplinary involving environmental science and computer science where the problem is formulated...

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Bibliographic Details
Published in:	The Science of the total environment Vol. 765; p. 144507
Main Authors:	Zhang, Bo, Zou, Guojian, Qin, Dongming, Lu, Yunjie, Jin, Yupeng, Wang, Hui
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 15.04.2021
Subjects:	Air pollutant prediction air pollutants computer science Deep learning Encoder-Decoder model environment Long short term memory neural networks Numerical analysis prediction Recurrent neural networks time series analysis Deep learning Air pollutant prediction Recurrent neural networks Encoder-Decoder model Numerical analysis Long short term memory
ISSN:	0048-9697, 1879-1026, 1879-1026
Online Access:	Get full text
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