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

Shuo Wang, Yanran Li, Jiang Zhang, Qingye Meng, Lingwei Meng +1 more
2/10/2020
eess.SPcs.LGeess.IV
gnn

Official code: shuowang-ai/PM2.5-GNN (228 stars, MIT, last commit 2026-10-14).

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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Code Implementations(1)

shuowang-ai/PM2.5-GNNOfficial100%
MIT

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

22871PythonOct 11, 20201 weeks agoMIT

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Cite this paper

@article{wang2020pmgnn,
  title  = {PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting},
  author = {Shuo Wang and Yanran Li and Jiang Zhang and Qingye Meng and Lingwei Meng and Fei Gao},
  year   = {2020},
  eprint = {2002.12898},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2002.12898}
}

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