Optimizing the Prediction Results of the CMAQ Model Using GRU Recurrent Neural Network
Improving the accuracy of air quality forecast is of great significance for precise prevention and control of regional air pollution.In this study,aiming at the air quality forecast in Xingtai area,the WRF-CMAQ-GRU model was established using the WRF meteorological model output and the CMAQ air quality model output combined with GRU recurrent neural network.Correction experiment were conducted to optimize the prediction of six pollutant in Xingtai City in July 2022,including PM2.5,PM10,SO2,NO2,O3,and CO.It was found that the optimization effect of the model on PM2.5 and O3 was the most obvious,and the overall correlation coefficient of PM2.5 increased from 0.28 to 0.85,the correlation coefficient of O3 increased from 0.29 to 0.70.It's preliminary verified that the GRU recurrent neural network can significantly optimize the prediction results of the WRF-CMAQ model,which greatly improves the accuracy of air quality prediction.
万方数据
维普
