An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2.5 concentration in urban environment

Abstract

This study proposes a new model for the spatiotemporal prediction of PM2.5 concentration at hourly and daily time intervals. It has been constructed on a combination of three-dimensional convolutional neural network and gated recurrent unit (3D CNN-GRU). The performance of the proposed model is boosted by learning spatial patterns from similar air quality (AQ) stations while maintaining long-term temporal dependencies with simultaneous learning and prediction for all stations over different time intervals. 3D CNN-GRU model was applied to air pollution observations, especially PM2.5 level, collected from several AQ stations across the city of Tehran, the capital of Iran, from 2016 to 2019. It could achieve promising results compared to the methods such as LSTM, GRU, ANN, SVR, and ARIMA, which are recently introduced in the literature; it estimates 84% (R2 = 0.84) and 78% (R2 = 0.78) of PM2.5 concentration variations for the next hour and the following day, respectively.