Self-adaptive spatial-temporal network based on heterogeneous data for air quality prediction

• Published in: Connection science Vol. 33; no. 3; pp. 427 - 446
• Main Authors: Chang, Feng, Ge, Liang, Li, Siyu, Wu, Kunyan, Wang, Yaqian
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.07.2021; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Air pollution; Air quality; Air quality prediction; Decision making; Deep learning; Feature extraction; graph convolutional network; Outdoor air quality; Spatial data; spatial-temporal dependencies
• ISSN: 0954-0091; 1360-0494; 1360-0494
• DOI: 10.1080/09540091.2020.1841095

With the development of society and the rise of people's environmental awareness, air pollution is receiving increased public attention. Accurate air quality prediction can provide useful information for government decision-making and residents' activities. However, accurately predicting future air quality remains a challenging task because of the complex spatial-temporal dependencies of air quality. Previous studies failed to explicitly model these spatial-temporal dependencies. In this paper, we propose a self-adaptive spatial-temporal network (SA-STNet) to efficiently and effectively capture the spatial-temporal dependencies of air quality. In order to effectively aggregate spatial information, we employ a self-adaptive graph convolution module that can learn the latent spatial correlations of air quality automatically. In the temporal dimension, we utilise three independent components to capture the recent, daily-periodic, and weekly-periodic temporal dependencies of air quality, respectively. In addition, our model exploits rich external complementary information by means of a features extraction component. A parametric-matrix-based fusion architecture is used to combine the outputs of different components into a joint representation which is used for generating the final prediction results. Extensive experiments carried out on real-world datasets demonstrate the outstanding performance of our model compared with baselines and state-of-the-art methods.