Research on prediction of air pollutant composition change based on deep learning

Chunyu Dai; Anjun Song

doi:10.1117/12.2682272

26 May 2023 Research on prediction of air pollutant composition change based on deep learning

Chunyu Dai, Anjun Song

Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 1270011 (2023) https://doi.org/10.1117/12.2682272
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

In recent years, the impact of air quality on people has gradually increased, and how to effectively analyze the changing laws of air pollutant components is of great significance. Due to the problems of vanishing gradients and memory constraints, most existing methods for long sequence prediction struggle to exploit the full contextual information of the sequence. This paper introduced an improved Autoformer model based on the autocorrelation mechanism, which divides the sequence into a periodic sequence and a cyclic trend sequence through time series decomposition, and further extracts deeper hidden features from the data by adding a feature extraction module to the architecture. Use the trend module to process the cyclic trend sequence to increase the model's perception of the trend. The experiments prove that the model has good prediction effect.

Citation Download Citation

Chunyu Dai and Anjun Song "Research on prediction of air pollutant composition change based on deep learning", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 1270011 (26 May 2023); https://doi.org/10.1117/12.2682272

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