Meteorological Visibility Prediction Based on GA-PSO-BP Neural Network
In view of the lack of meteorological visibility data in Anhui Province,meteorological data from four automatic meteorological stations(Mount Huangshan Station,Lingbi Station,Shannan Xigu Station,and Baize Lake Station)under different terrain conditions in Anhui Province were selected from the years 2017 to 2019.The meteorological elements closely related to visibility were first identified using the gray correlation analysis method.Subsequently,a hybrid algorithm of genetic algorithm(GA)and particle swarm optimization algorithm(PSO)was employed to optimize the prediction model of the back propagation(BP)neural network.The visibility of automatic weather stations under four different terrain conditions was predicted,and the prediction results were compared with the RF prediction model and XGBoost prediction model.The results indicate that,under any terrain condition,the GA-PSO-BP neural network prediction model exhibits smaller prediction errors and higher model accuracy.
万方数据
维普
