PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting
This work addresses air pollution prediction for environmental monitoring, representing an incremental improvement with domain-specific enhancements.
The paper tackles PM2.5 concentration forecasting by developing a graph neural network that incorporates domain knowledge to capture long-term dependencies, achieving validated effectiveness on a real-world dataset and deployment for free online service.
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.