Research and Practice of Deep Learning Model for Air Quality Prediction

Abstract:   Objective Timely and accurate air quality prediction data is very important for environmental management, especially during the period of heavy air pollution. The prediction data can provide data support for the decision-making of the government's ecological environment management departments to cope with the pollution situation and accurately allocate social resources. 
Methods The air quality prediction model AirNet6 developed by the author based on depth learning can give consideration to both accuracy and real-time performance to achieve 7-day or longer air quality prediction for ozone, sulfur dioxide, carbon monoxide and other factors.
Results Unlike traditional chemical model calculations, this model base on Spatio-Temporal Graph Convolutional Networks (STGCN), which captures the laws of historical monitoring data, weather prediction data, social activities and other data, and completes the prediction of more than one hundred points for the next 168 hours in two minutes.
Conclusions Experiments show that the AirNet6 model has made significant progress in speed, energy efficiency, and accuracy compared to traditional chemical models and time series AI models.