论文标题
PM2.5-GNN：域知识增强了PM2.5预测的图形神经网络
PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting
论文作者
论文摘要
在预测PM2.5浓度时，有必要考虑复杂的信息源，因为浓度在长期内受到各种因素的影响。在本文中，我们确定了PM2.5预测的一组关键领域知识，并开发了一种基于图形的新型模型PM2.5-GNN，能够捕获长期依赖性。在现实世界数据集上，我们验证了所提出的模型的有效性，并检查了其在PM2.5过程中捕获细粒和长期影响的能力。拟议的PM2.5-GNN也已在线部署，以提供免费的预测服务。
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.
