Deep Spatiotemporal Attention Network for Fine Particle Matter 2.5 Concentration Prediction With Causality Analysis

IEEE Access

Nohyoon Seong¹ 

[1] College of Business, Korea Advanced Institute of Science and Technology, Seoul, Republic of Korea;

关键词: Air pollution prediction;    causality analysis;    deep learning;    spatiotemporal deep learning;    transfer entropy;   

DOI  :  10.1109/ACCESS.2021.3080828

来源: DOAJ

【 摘 要 】

The increasing concentration of air pollutants, caused by industrialization and economic growth, is adversely affecting public health. Therefore, accurately measuring and predicting air pollution has been an important societal issue. With the era of big data and the development of artificial intelligence technologies, air pollution concentration is now being measured and recorded in real-time using different sensors. Studies have attempted to predict air pollution concentration using deep learning-based spatiotemporal prediction. This, in turn, is based on distance networks. In these studies, the distance network used to predict air pollution simply reflects the distance. However, since air pollutants cannot move over high mountain ranges and move according to the wind, the station network should include the effect of terrain and the wind direction. Previous studies do not consider these effects. To overcome these limitations, this study proposes a novel station network that combines distance and causality networks based on transfer entropy. To evaluate the performance of the proposed method, out-of-sample experiments with an hourly dataset are performed from January 2017 to October 2020 using information from 186 stations in the Republic of Korea. The results suggest that the proposed method showed state-of-the-art performance compared to existing distance-based algorithms.

【 授权许可】

Unknown