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

Sun, Xiaotong
Xu, Wei
Jiang, Hongxun
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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.
Decision Support for Smart Cities, Decision Analytics, Mobile Services, and Service Science, Air quality prediction, Deep learning, Emergency management, Recurrent neural network, Spatial-temporal framework
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