Spatio-temporal Prediction of Air Quality Using Spatio-temporal Clustering and Hierarchical Bayesian Model

Abstract

Air pollution can cause many negative impacts, so it is meaningful to establish an air quality prediction model with high accuracy for air pollution prevention and control. In previous studies, most models did not consider the spatio-temporal distribution characteristics of air quality. So this study proposes a novel spatio-temporal prediction approach STC-HBM, which combines spatio-temporal clustering (STC) and hierarchical Bayesian model (HBM) to build a spatio-temporal prediction model. In this study, we first perform spatio-temporal clustering on the Beijing-Tianjin-Hebei region in China and then apply hierarchical Bayesian models to different clusters separately. We consider three hierarchical Bayesian models: the separable spatio-temporal (SST) model, the Gaussian process (GP) model, and the autoregressive (AR) model. Since the prior distribution affects the prediction accuracy of the HBM, the resultant output of the Bayesian linear regression (BLR) model is used as the prior input of the HBM, which improves the flexibility of the model. The experimental results show that 13 cities in the BTH region are clustered into two clusters according to their spatio-temporal characteristics. Based on MAE, RMSE, MAPE, and coverage (CVG), cluster 1, and cluster 2 are better in both temporal and spatial prediction compared to the overall prediction model. In addition, for cluster 1, the models with the best prediction in time and space are AR and GP, respectively; for cluster 2, the models with the best prediction in time and space are SST and GP, respectively.