Deep learning architecture for air quality predictions

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 qu...

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Veröffentlicht in:	Environmental science and pollution research international Jg. 23; H. 22; S. 22408 - 22417
Hauptverfasser:	Li, Xiang, Peng, Ling, Hu, Yuan, Shao, Jing, Chi, Tianhe
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2016 Springer Nature B.V
Schlagworte:	Air Pollutants - chemistry Air pollution Air Pollution - analysis Air quality Algorithms Aquatic Pollution Artificial intelligence Atmospheric Protection/Air Quality Control/Air Pollution Deep learning Developing countries Earth and Environmental Science Ecotoxicology Environment Environmental Chemistry Environmental Health human health Humans industrialization LDCs Methods Models, Theoretical Neural networks Neural Networks (Computer) Outdoor air quality Pollutants prediction Prediction models Regression analysis Research Article Seasons Spatial discrimination learning Sustainable development Time series time series analysis Urbanization Waste Water Technology Water Management Water Pollution Control Beijing China China Deep learning Stacked autoencoder (SAE) Layer-wise pre-training Air quality prediction Spatiotemporal features BP algorithm
ISSN:	0944-1344, 1614-7499
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Zusammenfassung:	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.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0944-1344 1614-7499
DOI:	10.1007/s11356-016-7812-9