title: Deep learning architecture for air quality predictions.
abstract: With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.
authors.affiliation: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China.|University of Chinese Academy of Sciences, Beijing, 100049, China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Li
authors.affiliation: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China. pengling@radi.ac.cn.
authors.forename: Ling
authors.identifier: http://orcid.org/0000-0003-2999-9252
authors.initials: L
authors.lastname: Peng
authors.affiliation: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China.|University of Chinese Academy of Sciences, Beijing, 100049, China.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Institute of Electronics, Chinese Academy of Sciences, Beijing, 100190, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Shao
authors.affiliation: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China.
authors.forename: Tianhe
authors.initials: T
authors.lastname: Chi
mesh_terms: D000393:Air Pollutants / Q000737:chemistry*; D000397:Air Pollution / Q000032:analysis*; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012621:Seasons; D014507:Urbanization
chemical_list: D000393:Air Pollutants
keywords: Air quality prediction; BP algorithm; Deep learning; Layer-wise pre-training; Spatiotemporal features; Stacked autoencoder (SAE)