Deep spatial-temporal fusion network for fine-grained air pollutant concentration prediction

Abstract

Air pollution is a serious environmental problem that has attracted much attention. Predicting air pollutant concentration can provide useful information for urban environmental governance decision-making and residents’ daily health control. However, existing methods fail to model the temporal dependencies or have suffer from a weak ability to capture the spatial correlations of air pollutants. In this paper, we propose a general approach to predict air pollutant concentration, named DSTFN, which consists of a data completion component, a similar region selection component, and a deep spatial-temporal fusion network. The data completion component uses tensor decomposition method to complete the missing data of historical air quality. The similar region selection component uses region metadata to calculate the spatial similarity between regions. The deep spatial-temporal fusion network fuses urban heterogeneous data to capture factors affecting air quality and predict air pollutant concentration. Extensive experiments on a real-world dataset demonstrate that our model achieves the highest performance compared with state-of-the-art models for air quality prediction.