Multitask Air-Quality Prediction Based on LSTM-Autoencoder Model

With the development of the data-driven modeling techniques, using the neural network to simulate the transport process of atmospheric pollutants and constructing <inline-formula> <tex-math notation="LaTeX">{\textrm {PM}}_{2.5} </tex-math></inline-formula> time-seri...

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Bibliographic Details
Published in:	IEEE transactions on cybernetics Vol. 51; no. 5; pp. 2577 - 2586
Main Authors:	Xu, Xinghan, Yoneda, Minoru
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.05.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Air pollution Atmospheric modeling Atmospheric models Autoencoder Computer architecture Learning Logic gates long short-term memory (LSTM) Multilayers multitask learning Neural networks PM₂. Pollutants Pollution monitoring Prediction models Predictive models Simulation Stations Time series Time series analysis Urban areas
ISSN:	2168-2267, 2168-2275, 2168-2275
Online Access:	Get full text
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