PM2.5-GNN
Shuo Wang, Yanran Li, Jiang Zhang, Qingye Meng, Lingwei Meng, Fei Gao
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.
Topics & Concepts
Computer scienceGraphSet (abstract data type)Artificial intelligenceDomain (mathematical analysis)Data miningMachine learningContext (archaeology)Information systemGraph theoryAir Quality Monitoring and ForecastingAir Quality and Health ImpactsCOVID-19 impact on air quality