HighAir: A Hierarchical Graph Neural Network-Based Air Quality Forecasting Method

Understand

Accurately forecasting air quality is critical to protecting general public from lung and heart diseases.

• This is a challenging task due to the complicated interactions among distinct pollution sources and various other influencing factors.
• Existing air quality forecasting methods cannot effectively model the diffusion processes of air pollutants between cities and monitoring stations, which may suddenly deteriorate the air quality of a region.
• In this paper, we propose HighAir, i.e., a hierarchical graph neural network-based air quality forecasting method, which adopts an encoder-decoder architecture and considers complex air quality influencing factors, e.g., weather and land usage.