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arXiv · 2002.12898

PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting

Shuo Wang·Yanran Li·Jiang Zhang·Qingye Meng·Lingwei Meng·Fei Gao
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Abstract

When predicting PM2.5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period. In this paper, we identify a set of critical domain knowledge for PM2.5 forecasting and develop a novel graph based model, PM2.5-GNN, being capable of capturing long-term dependencies. On a real-world dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in PM2.5 process. The proposed PM2.5-GNN has also been deployed online to provide free forecasting service.

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Shuo Wang, Yanran Li, Jiang Zhang, Qingye Meng, Lingwei Meng, Fei Gao. 2020-02-10. PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting. https://doi.org/10.1145/3397536.3422208

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