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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Vydané v:IEEE transactions on cybernetics Ročník 51; číslo 5; s. 2577 - 2586
Hlavní autori: Xu, Xinghan, Yoneda, Minoru
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Shrnutí: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-series prediction model have become a hot topic. The existing data-driven approaches often ignore the dynamical relationships among multiple sites in urban areas, which results in nonideal prediction accuracy. In response to this problem, this article proposes a long short-term memory (LSTM) autoencoder multitask learning model to predict <inline-formula> <tex-math notation="LaTeX">{\textrm {PM}}_{2.5} </tex-math></inline-formula> time series in multiple locations city wide. The model could implicitly and automatically excavate the intrinsic relevance among the pollutants in different stations. And the meteorological information from the monitoring stations is fully utilized, which is beneficial for the performance of the proposed model. Specifically, multilayer LSTM networks can simulate the spatiotemporal characteristics of urban air pollution particles. And using the stacked autoencoder to encode the key evolution pattern of urban meteorological systems could provide important auxiliary information for <inline-formula> <tex-math notation="LaTeX">{\textrm {PM}}_{2.5} </tex-math></inline-formula> time-series prediction. In addition, multitask learning could automatically discover the dynamical relationship between multiple key pollution time series and solve the problem of insufficient use of multisite information in the modeling process of the traditional data-driven methods. The simulation results of <inline-formula> <tex-math notation="LaTeX">{\textrm {PM}}_{2.5} </tex-math></inline-formula> prediction in Beijing indicate the effectiveness of the proposed method.
Bibliografia:ObjectType-Article-1
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2019.2945999