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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Veröffentlicht in:	IEEE access 2020, Vol.8, p.9864-9874
Hauptverfasser:	Chang, Shuo-Wen, Chang, Chung-Ling, Li, Long-Tin, Liao, Shih-Wei
Format:	Artikel
Sprache:	eng
Schlagworte:	Air pollution Algorithms Artificial neural networks Atmospheric modeling autoregressive integrated moving average Autoregressive models Autoregressive processes Data models Machine learning neural network Neural networks Prediction models Predictive models Q-learning Reinforcement learning Smart city smart environments Time lag Time series urban computing White noise
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