An end-to-end air pollutant concentrations prediction algorithm based on attention mechanism for targeted season: A case study in North China

Accurate air pollutant concentrations prediction allows effective environment management to reduce the impact of pollution. The encoder-decoder model based on long short-term memory (LSTM) demonstrated great potential in air pollutant concentrations prediction. However, the influence of the hidden vector on the output of the decoder at each moment may be different during the long time-series prediction problem. In this paper, an attention mechanism is introduced in the decoder part to further improve the final prediction of the pollutant concentrations by filtering out some noise. In the experimental stage, we exploited the data collected from five representative cities in North China (Beijing, Tianjin, Shijiazhuang, Taiyuan, and Baotou) from 2014 to 2019 as the experimental dataset, and added the auxiliary data unique to North China. Then we divided the datasets into four seasonal datasets (spring, summer, autumn, and winter) to obtain targeted seasonal prediction models for the different seasons. The experimental results show that the model can predict the future trend of the pollutant concentration in a certain season relatively accurately.