  Citywide Air Pollution Forecasting tries to precisely predict the air quality
multiple hours ahead for the entire city. This topic is challenged since air
pollution varies in a spatiotemporal manner and depends on many complicated
factors. Our previous research has solved the problem by considering the whole
city as an image and leveraged a Convolutional Long Short-Term Memory
(ConvLSTM) model to learn the spatiotemporal features. However, an image-based
representation may not be ideal as air pollution and other impact factors have
natural graph structures. In this research, we argue that a Graph Convolutional
Network (GCN) can efficiently represent the spatial features of air quality
readings in the whole city. Specially, we extend the ConvLSTM model to a
Spatiotemporal Graph Convolutional Recurrent Neural Network (Spatiotemporal
GCRNN) model by tightly integrating a GCN architecture into an RNN structure
for efficient learning spatiotemporal characteristics of air quality values and
their influential factors. Our extensive experiments prove the proposed model
has a better performance compare to the state-of-the-art ConvLSTM model for air
pollution predicting while the number of parameters is much smaller. Moreover,
our approach is also superior to a hybrid GCN-based method in a real-world air
pollution dataset.
