Dual-channel spatial-temporal difference graph neural network for PM2.5 forecasting

Xiaocao Ouyang, Yan Yang, Yiling Zhang, Wei Zhou, Dongyu Guo

Abstract: Accurate PM $$_{2.5}$$ 2.5 forecasting is significant for improving quality of life and human health. However, it is very challenging to capture the high spatiotemporal correlations and the complex diffusion processes of PM $$_{2.5}$$ 2.5 . Most existing PM $$_{2.5}$$ 2.5 prediction methods only focus on spatiotemporal dependencies. In addition, the PM $$_{2.5}$$ 2.5 diffusion process with domain knowledge in deep learning is rarely considered. Therefore, how to simultaneously capture comprehensive spatiotemporal dependencies and model the complicated diffusion process of PM $$_{2.5}$$ 2.5 is still a challenge. To address this problem, we propose a dual-channel spatial–temporal difference graph neural network (DC-STDGN) to forecast future PM $$_{2.5}$$ 2.5 concentrations. DC-STDGN first constructs a dual-channel structure to obtain distance-based local neighboring information and the global hidden spatial correlation of the data. Then, a temporal convolution layer is designed to handle the long-term dependency. Finally, the spatial difference with domain knowledge is introduced to model the complex diffusion process and capture more comprehensive spatiotemporal correlations. The extensive experiments with three real-world datasets demonstrate the improved prediction performance of DC-STDGN over state-of-the-art baselines. DC-STDGN outperforms the second-best model by up to 16.9% improvement in mean absolute error, 8.9% improvement in root mean square error and 18.2% improvement in mean absolute scaled error.