Air quality index prediction model integrating multi-head self-attention mechanism

Lantao Yao; Lizhi Liu

doi:10.1117/12.2689367

10 August 2023 Air quality index prediction model integrating multi-head self-attention mechanism

Lantao Yao, Lizhi Liu

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 1274819 (2023) https://doi.org/10.1117/12.2689367
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

Aiming at the problem of insufficient accuracy of existing models in the process of air quality index prediction, an air quality index (AQI) prediction model (CALSTM) incorporating a multi-head self-attention mechanism is proposed based on Long Short-Term Memory network (LSTM). The model effectively extracts low-dimensional features of air pollutant concentrations and meteorological data related to the air quality index through Convolutional Neural Network (CNN) and uses LSTM to fully reflect the long-term historical process in the input time series. To improve the acquisition ability of the global information of the model context, the multi-head self-attention mechanism is used to extract the hidden information of the time series at different levels and improve the model’s generalization ability. In addition, to further improve the prediction accuracy of the model, an AQI time series difference method is proposed based on the data correlation analysis. The experimental results show that the CALSTM using the AQI time series difference method has achieved an effect of 16.27% on MAPE, and on the indicators of MAE, MSE, RMSE, and R2, they are 6.99, 114.79, 10.71, and 0.9586, respectively. Compared with LSTM has achieved better prediction results.

Citation Download Citation

Lantao Yao and Lizhi Liu "Air quality index prediction model integrating multi-head self-attention mechanism", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 1274819 (10 August 2023); https://doi.org/10.1117/12.2689367

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KEYWORDS

Data modeling

Air quality

Atmospheric modeling

Feature extraction

Data hiding

Convolution

Correlation coefficients

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