An intelligent hybrid model for air pollutant concentrations forecasting: Case of Beijing in China

Abstract

The forecasting of air pollutant concentrations is of great significance to protect the environment and guarantee the health of people. In the study, a novel hybrid model, namely EWT-MAEGA-NARX combining the EWT, MAEGA and NARX neural networks, is put forward for multi-step air pollutant concentrations forecasting. Four types of air pollutant containing PM2.5, SO2, NO2, and CO in Beijing, China are selected to verify the accuracy of the proposed model. To inspect the forecasting performance of the proposed model, some other models are chosen as the comparison models, which comprise of the VMD-MAEGA-NARX model, EWT-MAEGA-SVM model, MAEGA-NARX model, EWT-NARX model and EWT-ARIMA-NARX model. The experimental results show that: (1) The EWT-MAEGA-NARX model can achieve satisfactory predictions in air pollutant concentrations forecasting, whose MAE in 1-step forecasting of PM2.5, SO2, NO2, CO series are 0.1314 μ g/ m3, 0.0213 μ g/ m3, 0.0722 μ g/ m3, 0.0033 mg/ m3, respectively. (2) In the EWT-MAEGA-NARX model, the EWT is a good feature extractor and the parameter optimization process of MAEGA for the NARX neural network can obviously enhance the prediction performance of the model.