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

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dc.contributor.authorSun, Xiaotong
dc.contributor.authorXu, Wei
dc.contributor.authorJiang, Hongxun
dc.date.accessioned2019-01-02T23:50:57Z
dc.date.available2019-01-02T23:50:57Z
dc.date.issued2019-01-08
dc.description.abstractTo 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.extent10 pages
dc.identifier.doi10.24251/HICSS.2019.155
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59567
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDecision Support for Smart Cities
dc.subjectDecision Analytics, Mobile Services, and Service Science
dc.subjectAir quality prediction, Deep learning, Emergency management, Recurrent neural network, Spatial-temporal framework
dc.titleSpatial-temporal prediction of air quality based on recurrent neural networks
dc.typeConference Paper
dc.type.dcmiText

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