Bilstm-bigru: a fusion deep neural network for predicting air pollutant concentration

Predicting air pollutant concentrations is an efficient way to prevent incidents by providing early warnings of harmful air pollutants. A precise prediction of air pollutant concentrations is an important factor in controlling and preventing air pollution. In this paper, we develop a bidirectional long-short-term memory and a bidirectional gated recurrent unit (BiLSTM-BiGRU) to predict PM2.5 concentrations in a target city for different lead times. The BiLSTM extracts preliminary features, and the BiGRU further extracts deep features from air pollutant and meteorological data. The fully connected (FC) layer receives the output and makes an accurate prediction of the PM2.5 concentration. The model is then compared with five other deep learning models in terms of root mean square error (RMSE), mean absolute error (MAE) and correlation (R-2) over different lead times. The results indicate that the proposed model has at least 2.2 times lower RMSE than the other models.