The problem of air pollution threatens public health. Air quality forecasting
can provide the air quality index hours or even days later, which can help the
public to prevent air pollution in advance. Previous works focus on citywide
air quality forecasting and cannot solve nationwide city forecasting problem,
whose difficulties lie in capturing the latent dependencies between
geographically distant but highly correlated cities. In this paper, we propose
the group-aware graph neural network (GAGNN), a hierarchical model for
nationwide city air quality forecasting. The model constructs a city graph and
a city group graph to model the spatial and latent dependencies between cities,
respectively. GAGNN introduces differentiable grouping network to discover the
latent dependencies among cities and generate city groups. Based on the
generated city groups, a group correlation encoding module is introduced to
learn the correlations between them, which can effectively capture the
dependencies between city groups. After the graph construction, GAGNN
implements message passing mechanism to model the dependencies between cities
and city groups. The evaluation experiments on Chinese city air quality dataset
indicate that our GAGNN outperforms existing forecasting models.

本研究提出了一种基于群组感知的图神经网络 (GAGNN) 模型，用于预测全国城市的空气质量，该模型通过构建城市图和城市群组图来建模城市之间的空间和潜在依赖关系，并采用消息传递机制来模拟城市和城市群组之间的依赖关系，实验证明该模型优于现有的预测模型。