CombineDeepNet: A Deep Network for Multistep Prediction of Near-Surface PM$_{2.5}$ Concentration

PM$_{2.5}$ is a type of air pollutant that can cause respiratory and cardiovascular problems. Precise PM$_{2.5}$ ($\mu {\text {g/m}} ^{3}$) concentration prediction may help reduce health concerns and provide early warnings. To better understand air pollution, a number of approaches have been presented for predicting PM$_{2.5}$ concentrations. Previous research used deep learning models for hourly predictions of air pollutants due to their success in pattern recognition, however, these models were unsuitable for multisite, long-term predictions, particularly in regard to the correlation between pollutants and meteorological data. This article proposes the combine deep network (CombineDeepNet), which combines multiple deep networks, including a bidirectional long short-term memory, bidirectional gated recurrent units, and a shallow model represented by fully connected layers, to create a hybrid forecasting system. It can effectively capture the complex relationships between air pollutants and various influencing factors to predict hourly PM$_{2.5}$ concentrations in multiple monitoring sites based in China. The best root mean square error achieved was 22.0 $\mu {\text {g/m}} ^{3}$ (long-term) and 6.2 $\mu {\text {g/m}} ^{3}$ (short-term), with mean absolute error values of 3.4 $\mu {\text {g/m}} ^{3}$ (long-term) and 2.2 $\mu {\text {g/m}} ^{3}$ (short-term). In addition, the correlation coefficient (R$^{2}$) reached 0.96 (long-term) and 0.83 (short-term) across six monitoring sites. These results demonstrate that CombineDeepNet enhances prediction accuracy compared with popular deep learning methods. Therefore, CombineDeepNet proves to be a important framework for predicting PM$_{2.5}$ concentration.