For the aged: A novel PM2.5 concentration forecasting method based on spatial-temporal graph ordinary differential equation networks in home-based care parks

Abstract

The immune ability of the elderly is not strong, and the functions of the body are in a stage of degeneration, the ability to clear PM2.5 is reduced, and the cardiopulmonary system is easily affected. Accurate prediction of PM2.5 can provide guidance for the travel of the elderly, thereby reducing the harm of PM2.5 to the elderly. In PM2.5 prediction, existing works usually used shallow graph neural network (GNN) and temporal extraction module to model spatial and temporal dependencies, respectively, and do not uniformly model temporal and spatial dependencies. In addition, shallow GNN cannot capture long-range spatial correlations. External characteristics such as air humidity are also not considered. We propose a spatial-temporal graph ordinary differential equation network (STGODE-M) to tackle these problems. We capture spatial-temporal dynamics through tensor-based ordinary differential equation, so we can build deeper networks and exploit spatial-temporal features simultaneously. In addition, in the construction of the adjacency matrix, we not only used the Euclidean distance between the stations, but also used the wind direction data. Besides, we propose an external feature fusion strategy that uses air humidity as an auxiliary feature for feature fusion, since air humidity is also an important factor affecting PM2.5 concentration. Finally, our model is evaluated on the home-based care parks atmospheric dataset, and the experimental results show that our STGODE-M can more fully capture the spatial-temporal characteristics of PM2.5, achieving superior performance compared to the baseline. Therefore, it can provide better guarantee for the healthy travel of the elderly.