Air quality prediction based on the GCN-GRU model with CEEMDAN decomposition

Air pollution significantly impacts human health, the economy, and social stability. Accurate air quality prediction is crucial for effective prevention and control of air pollution. To address the highly nonlinear and non-stationary nature of PM2.5 sequences and the spatiotemporal dependencies between the sequences and air quality monitoring stations, a spatiotemporal hybrid prediction model based on CEEMDAN-GCN-GRU is proposed.First, PM2.5 sequences from each air quality monitoring station are decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to obtain multiple intrinsic mode functions (IMFs) and residual components (RES). Next, each component is combined with other features and input into the Graph Convolution Network (GCN) to capture the spatial correlations between PM2.5 concentrations at different monitoring stations. The combined decomposed PM2.5 sequence data and its spatial correlations are then used as input to the Gated Recurrent Unit (GRU) to extract spatiotemporal features. Finally, the individual sub-sequences output from the GRU are predicted using a linear regression layer, and the results are superimposed to obtain the final PM2.5 prediction results.Experiments conducted with 10 state-controlled air quality monitoring stations in Dalian city show that the proposed model outperforms comparative models in terms of root mean square error (RMSE) and mean absolute error (MAE), achieving reductions of 33.86% and 46.04%, respectively, compared to single benchmark LSTM and GRU models.