融合GNSS气象参数的BP神经网络雾霾预测研究

结合IGS中心获取的BJFS站气象参数（气温（T）、气压（P）、大气可降水量（PWV））及同期PM_2.5数据，建立一种融合时序网络和回归网络的雾霾预测模型，对PM_2.5浓度进行预测。研究表明，引入GNSS气象参数的融合网络模型较单一网络模型适应性强、准确度高，在一定精度范围内可准确预测PM_2.5的变化，时效性达3 h。本文结论验证了卫星导航技术应用于雾霾天气监测及预报的可行性。

Study on Haze Prediction of BP Neural Network Incorporating GNSS Meteorological Parameters

Base on the meteorological parameters (temperature (T), air pressure (P), and precipitable water vapor (PWV)), of Beijing Fangshan Station released by the IGS Center andPM_2.5 data for the same period, this paper establishes a haze prediction model combining time series network and regression network to predictPM_2.5 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 ofPM_2.5 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.