Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting
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ID: 282783
2021
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Abstract
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.
| Reference Key |
zhang2021groupaware
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| Authors | Ling Chen; Jiahui Xu; Binqing Wu; Yuntao Qian; Zhenhong Du; Yansheng Li; Yongjun Zhang |
| Journal | arXiv |
| Year | 2021 |
| DOI |
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