Encoder-Decoder Model for Forecast of PM2.5 Concentration per Hour

Abstract

Atmospheric PM2.5 is a major pollutant impacting on human health and the environment. Based on traditional neural networks, we construct three prediction models: BP, Stack GRU, and Encoder-Decoder. We use Tianjin's continuous 171-day hourly PM2.5 concentration, air quality index, and meteorological data to train and test the model and evaluate the prediction performance of the model. In the experiment, using the meteorological factors, pollutant factors, seasonal factors, and PM2.5 concentrations as inputs, the PM2.5 concentration of every hour of the next day is predicted. The experimental result shows that when using PM2.5 concentration data for 3 days per hour to predict PM2.5 per hour, continuous forecasting for 43 days, the PM2.5 concentration value predicted by the Encoder-Decoder model is not significantly different from the value of PM2.5 issued by Tianjin local authorities, and the root mean square error (RMSE) is 43.17. With the same input data, the prediction result of Encoder-Decoder model is better than BP neural network and GRU prediction model, which shows that Encoder-Decoder model has better adaptability in predicting PM2.5 concentration than BP neural network and GRU model.