PM2.5 concentration prediction based on CNN-GRU model fused with Luong attention

Zhen Wang; Lizhi Liu

doi:10.1117/12.2689345

10 August 2023 PM_2.5 concentration prediction based on CNN-GRU model fused with Luong attention

Zhen Wang, Lizhi Liu

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127481C (2023) https://doi.org/10.1117/12.2689345
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

In order to improve the accuracy of PM_2.5 concentration prediction, a CNN-GRU deep learning model based on fusion of Luong Attention is proposed. Firstly, the correlation between various air pollutants and meteorological factors and PM_2.5 concentration is comprehensively analyzed, and the high correlation data is formed into a feature set. Secondly, the feature set is input into CNN for feature dimensioning, and then the output results of each time step are extracted through GRU. Finally, by introducing the Luong attention mechanism, the attention scores of the hidden states at each position of the output sequence are calculated, and the context vector is weighted to highlight the input step information that plays a key role in the prediction of PM_2.5 concentration. The results show that using the CNN-GRU model with attention mechanism to predict the PM_2.5 concentration in the next 24 hours, compared with the machine model and other deep learning models, RMSE and MAE have a certain decline, and have a higher generalization ability.

Citation Download Citation

Zhen Wang and Lizhi Liu "PM_2.5 concentration prediction based on CNN-GRU model fused with Luong attention", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127481C (10 August 2023); https://doi.org/10.1117/12.2689345

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KEYWORDS

Data modeling

Atmospheric modeling

Meteorology

Deep learning

Roads

Convolution

Air quality

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