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Spatial-temporal prediction of air quality based on recurrent neural networks

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dc.contributor.author Sun, Xiaotong
dc.contributor.author Xu, Wei
dc.contributor.author Jiang, Hongxun
dc.date.accessioned 2019-01-02T23:50:57Z
dc.date.available 2019-01-02T23:50:57Z
dc.date.issued 2019-01-08
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59567
dc.description.abstract To predict air quality (PM2.5 concentrations, et al), many parametric regression models have been developed, while deep learning algorithms are used less often. And few of them takes the air pollution emission or spatial information into consideration or predict them in hour scale. In this paper, we proposed a spatial-temporal GRU-based prediction framework incorporating ground pollution monitoring (GPM), factory emissions (FE), surface meteorology monitoring (SMM) variables to predict hourly PM2.5 concentrations. The dataset for empirical experiments was built based on air quality monitoring in Shenyang, China. Experimental results indicate that our method enables more accurate predictions than all baseline models and by applying the convolutional processing to the GPM and FE variables notable improvement can be achieved in prediction accuracy.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Decision Support for Smart Cities
dc.subject Decision Analytics, Mobile Services, and Service Science
dc.subject Air quality prediction, Deep learning, Emergency management, Recurrent neural network, Spatial-temporal framework
dc.title Spatial-temporal prediction of air quality based on recurrent neural networks
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2019.155
Appears in Collections: Decision Support for Smart Cities


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