Abstract:Base on the meteorological parameters (temperature (T), air pressure (P), and precipitable water vapor (PWV)), of Beijing Fangshan Station released by the IGS Center and PM2.5 data for the same period, this paper establishes a haze prediction model combining time series network and regression network to predict PM2.5 concentration. The research shows that the fusion network model introducing GNSS meteorological parameters is more adaptable and accurate than the single network model, that it can accurately predict the change of PM2.5 within a certain accuracy range, and that timeliness can reach 3 h. Related studies have verified the feasibility of satellite navigation technology for monitoring and forecasting of haze weather.
ZHOU Yongjiang,YAO Yibin,YAN Xiao et al. Study on Haze Prediction of BP Neural Network Incorporating GNSS Meteorological Parameters[J]. jgg, 2019, 39(11): 1148-1152.