PM2.5 concentration prediction based on temporal convolutional network

Fei Lei; Xuan Zhang; Yuning Yang

doi:10.1117/12.2640872

13 October 2022 PM2.5 concentration prediction based on temporal convolutional network

Fei Lei, Xuan Zhang, Yuning Yang

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871W (2022) https://doi.org/10.1117/12.2640872
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Air pollution is an environmental problem facing mankind today. Therefore, predicting the concentration of air pollutants in advance plays an important role in people's life and government decision-making. In this paper, a multi-channel asymmetric structure prediction model based on temporal convolutional network (TCN) is proposed. As TCN omits some feature information when learning time series features, increasing the number of channels will improve the receptive field of the model, cover longer historical information and extract more time series features. The influence of meteorological factors on the concentration of air pollutants is fully considered in the prediction model, which is used as an auxiliary factor to improve the prediction performance of the model. The concentration of air pollutants collected from the air monitoring station in Fushun City, Liaoning Province, is used as the data set to verify the effectiveness of the model, and the experimental comparison with other prediction models is conducted. The results show that the model proposed in this paper has more accurate prediction accuracy and stronger stability.

Citation Download Citation

Fei Lei, Xuan Zhang, and Yuning Yang "PM2.5 concentration prediction based on temporal convolutional network", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871W (13 October 2022); https://doi.org/10.1117/12.2640872

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