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

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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Bibliographic Details
Main Authors: Jun Ma, Yuexiong Ding, Vincent J. L. Gan, Changqing Lin, Zhiwei Wan
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Air pollution
machine learning
neural networks
spatiotemporal phenomena
deep learning
long short-term memory
Online Access:https://ieeexplore.ieee.org/document/8784234/

Internet

https://ieeexplore.ieee.org/document/8784234/

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