Urban PM2.5 prediction based on temporal convolution network

Ting Shi; Wu Yang; Junfei Qiao

doi:10.1117/12.2615130

15 October 2021 Urban PM2.5 prediction based on temporal convolution network

Ting Shi, Wu Yang, Junfei Qiao

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119330G (2021) https://doi.org/10.1117/12.2615130
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

Air pollution has been plaguing human society and seriously affects the normal activities of human beings. PM2.5 is a kind of air suspension particles with complex composition, which is one of the main components of air pollution and poses serious threat to human health. In this paper, a model based Temporal Convolution Network (TCN) was applied to predict outdoor PM2.5 in Beijing. Model based on TCN is capable to parallel operations and has flexible convolution kernel compared with models based on other neural networks such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU) and traditional Convolutional Neural Network (CNN). Additionally, the model was verified in the experiment and the results indicated that the model achieved better performance than traditional models.

Citation Download Citation

Ting Shi, Wu Yang, and Junfei Qiao "Urban PM2.5 prediction based on temporal convolution network", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119330G (15 October 2021); https://doi.org/10.1117/12.2615130

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KEYWORDS

Convolution

Performance modeling

Data modeling

Neural networks

Atmospheric modeling

Air contamination

Atmospheric particles

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