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
Online-Zugang:	Volltext
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