Prediction of Air Pollutant Concentration Based on Luong Attention Mechanism Seq2Seq Model

In view of the low accuracy and unstable effect of traditional deep learning prediction model in air quality prediction, this paper proposes a Sequence to Sequence (seq2seq) model based on the Luong attention mechanism. Firstly, this paper uses data of daily observation from 41 observation stations in Chongqing from 2017 to 2018 to conduct prediction experiments, and analyze the correlation between environmental factors and each pollutant concentration. Then use the Horlzworth's dry adiabatic curve method to find the maximum mixed layer thickness and add it to environmental factors to predict air quality. Finally, the average absolute error (MAE) and root mean square error (RMSE) are used as evaluation indicators. Compared with other prediction models, the results show that the Seq2Seq model based on Luong attention mechanism with the maximum mixed layer thickness has the best performance.