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.
翻译:空气污染问题威胁着公众健康。空气质量预报可以提供空气质量指数小时,甚至几天后,帮助公众提前防止空气污染。以前的工作侧重于全市空气质量预报,无法解决全国城市预测问题,而城市预测问题的困难在于捕捉地理位置遥远但关系密切的城市之间的潜在依赖性。在本文中,我们建议采用群体觉测图神经网络(GAGNN),这是全国城市空气质量预报的等级模式。模型可以构建一个城市图和一个城市群图,分别用于模拟城市之间的空间和潜在依赖性。GAGNN采用不同的分组网络,以发现城市之间的潜在依赖性,并产生城市群。根据生成的城市群,引入了一个群体关联编码模块,以了解它们之间的关联性,从而有效地捕捉城市群体之间的依赖性。在绘制图表后,GAGNNN实施信息传递机制,以模拟城市和城市群体之间的依赖性。中国城市空气质量数据设置的评估实验表明,我们GAGNNN的模型比现有的预测模型要好。