The application of deep learning method in Shanghai PM2.5 prediction
MA Jing-hui1,2,3, CAO Yu1,3, YU Zhong-qi1,3, QU Yuan-hao1,3, XU Jian-ming1,3
1. Yangtze River Delta Center for Environmental Meteorology Prediction and Warning, Shanghai 200030, China;
2. Fudan University, Shanghai 200433, China;
3. Shanghai Key Laboratory of Meteorology and Health, Shanghai 200030, China
In order to improve the forecasting ability of PM2.5 concentration, especially the forecasting ability of PM2.5 heavy pollution, this study was based on the mesoscale meteorological-chemical coupled modeling system (WRF-Chem) forecast data in combination with weather and PM2.5 (fine particulate matter) observational data, a machine learning model of PM2.5 prediction in Shanghai, China was established. The results showed that the machine learning algorithm combined with WRF-Chem prediction obviously corrected the deviation of model prediction due to the non-objectivity of the model, and improved the prediction effect. The linear regression method (Lasso) could not obtain a suitable optimization effect, and the deep learning sequence to sequence algorithm was selected to improve the model and verified by experiments. The overall correlation coefficient of the method for the PM2.5 prediction increased from 0.51 to 0.79. The root mean square error was reduced from 25.9μg/m3 to 15.0μg/m3. The Seq2seq modified PM2.5 forecasting model based on WRF-Chem can effectively improve the prediction accuracy. It took a useful application prospect in air quality forecast.
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