Spatiotemporal Prediction of PM2.5 Concentrations at Different Time Granularities Using IDW-BLSTM

As air pollution becomes an increasing concern globally, governments, and research institutions have attached great importance to air quality prediction to help give early warnings and prevent the impacts of air pollution. The existing prediction methods for air quality forecasting include determini...

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Veröffentlicht in:	IEEE access 2019, Vol.7, p.107897-107907
Hauptverfasser:	Ma, Jun, Ding, Yuexiong, Gan, Vincent J. L., Lin, Changqing, Wan, Zhiwei
Format:	Artikel
Sprache:	eng
Schlagworte:	Air pollution Air quality Artificial neural networks Atmospheric modeling Deep learning inverse distance weighting long short-term memory Machine learning Mathematical models Monitoring neural networks Outdoor air quality Performance prediction Pollutants Predictive models Research facilities Spatial distribution spatiotemporal phenomena Stations Statistical methods
Online-Zugang:	Volltext
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