Reinforcement Learning for Improving the Accuracy of PM2.5 Pollution Forecast Under the Neural Network Framework

Reinforcement Learning for Improving the Accuracy of PM2.5 Pollution Forecast Under the Neural Network Framework

Air pollution seriously damages human health on a large scale. Although earlier works have improved a variety of predictive models of air pollution, the ability to accurately predict air pollution indices remains elusive. Time series prediction plays an important role in many fields. Some predecesso...

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Bibliographic Details
Main Authors: Shuo-Wen Chang, Chung-Ling Chang, Long-Tin Li, Shih-Wei Liao
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Smart city
smart environments
urban computing
autoregressive integrated moving average
time series
neural network
Online Access:https://ieeexplore.ieee.org/document/8784042/

Internet

https://ieeexplore.ieee.org/document/8784042/

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