Deep learning architecture for air quality predictions

	Deep learning architecture for air quality predictions
	Li, Xiang 1; Peng, Ling 1; Hu, Yuan 1; Shao, Jing 1; Chi, Tianhe 1
刊名	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
	2016
卷号	23 期号:22 页码:22408-22417
关键词	SEA-LEVEL RISE C-BAND DEMS INUNDATION ICESAT CHINA
通讯作者	Peng, L (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China.
英文摘要	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.
学科主题	Environmental Sciences & Ecology
类目[WOS]	Environmental Sciences
收录类别	SCI
语种	英语
WOS记录号	WOS:000387602800014
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39262]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China 2.Chinese Acad Sci, Inst Elect, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Li, Xiang,Peng, Ling,Hu, Yuan,et al. Deep learning architecture for air quality predictions[J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,2016,23(22):22408-22417.
APA	Li, Xiang,Peng, Ling,Hu, Yuan,Shao, Jing,&Chi, Tianhe.(2016).Deep learning architecture for air quality predictions.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,23(22),22408-22417.
MLA	Li, Xiang,et al."Deep learning architecture for air quality predictions".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 23.22(2016):22408-22417.