PM2.5-GNN: PP2.5-GNN: 预测PP2.5的域知识增强图形神经网络 (PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting)

from arxiv, Pre-print version of a ACM SIGSPATIAL 2020 poster [paper](https://dl.acm.org/doi/10.1145/3397536.3422208). The code is available at [Github](https://github.com/shawnwang-tech/PM2.5-GNN), and the talk is available at [YouTube](https://www.youtube.com/watch?v=VX93vMthkGM)

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.

翻译：在预测PM2.5浓度时,有必要考虑复杂的信息来源,因为浓度长期受各种因素的影响。在本文中,我们为PM2.5预测确定了一套关键领域知识,并开发了一个基于图表的新颖模型,即PM2.5-GNN,能够捕捉长期依赖性。在真实世界的数据集中,我们验证了拟议模型的有效性,并审查了其在PM2.5进程中捕捉微粒和长期影响的能力。拟议的PM2.5-GNN也在线部署,以提供免费的预测服务。