Adaptive scalable spatio-temporal graph convolutional network for PM2.5 prediction

Abstract

PM2.5 Prediction is a complex task of large-scale spatio-temporal analysis, which not only needs comprehension of static geospatial knowledge and relative features but also needs to analyze the real-time situation. This paper discusses the characteristics of the static graph and the dynamic graph in spatio-temporal series tasks. An Adaptive Scalable Spatio-temporal Graph Convolutional Network(ASGCN) model is proposed to predict PM2.5. To capture and analyze the characteristics of the time series period of PM2.5, a time convolution network based on the strategies of inception and gating is proposed and used as a temporal module. A dynamic graph idea is adopted to distinguish the spatio-temporal similarity of different periods. And an adaptive weighted multilayer graph convolution network is used to process static and dynamic graphs, aiming to analyze the spatial relationship of PM2.5 stations. The convolution network with the inception and gating improves the time-series feature capture ability, and adaptive static and dynamic graphs enhance the spatial relationship analysis ability. The temporal and spatial modules of the model are relatively independent, which benefits obtaining the potential information of datasets to improve the prediction accuracy. At the same time, these modules cooperate to make the model adaptable to various data. We choose a great number of comparative models and design a thorough experimental scheme including single-step prediction, multi-step prediction, hyperparameter experiments, and ablation experiments on two real PM2.5 datasets collected in China. Finally, the model achieves performance close to or better than the current state-of-the-art models selected for comparison in prediction tasks.