Paper Info
Title
Modeling Inter-station Relationships with Attentive Temporal Graph Convolutional Network for Air Quality Prediction
Abstract
ABSTRACTAir pollution is an important environmental issue of increasing concern, which impacts human health. Accurate air quality prediction is crucial for avoiding people suffering from serious air pollution. Most of the prior works focus on capturing the temporal trend of air quality for each monitoring station. Recent deep learning based methods also model spatial dependencies among neighboring stations. However, we observe that besides geospatially adjacent stations, the stations which share similar functionalities or consistent temporal patterns could also have strong dependencies. In this paper, we propose an Attentive Temporal Graph Convolutional Network (ATGCN) to model diverse inter-station relationships for air quality prediction of citywide stations. Specifically, we first encode three types of relationships among stations including spatial adjacency, functional similarity, and temporal pattern similarity into graphs. Then we design parallel encoding modules, which respectively incorporate attentive graph convolution operations into the Gated Recurrent Units (GRUs) to iteratively aggregate features from related stations with different graphs. Furthermore, augmented with an attention-based fusion unit, decoding modules with a similar structure to the encoding modules are designed to generate multi-step predictions for all stations. The experiments on two real-world datasets demonstrate the superior performance of our model beyond state-of-the-art methods.
Year
DOI
Venue
2021
10.1145/3437963.3441731
Web Search and Data Mining
DocType
Citations 
PageRank 
Conference
2
0.40
References 
Authors
0
5
Name
Order
Citations
PageRank
Chunyang Wang121.07
Yanmin Zhu21767142.50
Tianzi Zang332.09
Haobing Liu422.76
Jiadi Yu537157.86