A multi-factor PWV prediction model based on MLP neural network for southern China
To address the problem related to the effects on the precision in the inversion of precipitable water vapor(PWV)using global navigation satellite system(GNSS)arisen by the necessity of obtaining key parameters such as atmospheric weighted mean temperature(Tm),the correlation between PWV and based on the correlation between PWV and the tropospheric zenith wet delay(ZWD)and other factors was investigated.The 2015-2017 sounding data from 40 sounding stations in southern China to establish the MLP model,the linear regression model(LRM)and the multiple regression fitting algorithm to predict PWV based on the multi-layer perceptron(MLP)neural network.MLP model,linear regression(LR)model and nonlinear regression(NLR)model were developed respectively based on the multilayer perceptron(MLP)neural network and multiple regression fitting algorithm.To fully investigate the influence of the two modeling methods on the accuracy of PWV,the accuracy of the model was examined using the 2018 sounding data as the reference value and compared with the traditional PWV prediction model(PWV-SC2 model).The results show that the average annual RMSE,bias,and RE of the MLP model are 0.66 mm,0.06 mm,and 2.18%,respectively,which are 0.11 mm(14.6%)and 0.17 mm(20.5%)lower compared to the LR model and the NLR model in terms of average annual RMSE,0.04 mm(43.7%)and 0.28 mm(82.3%)in terms of average annual bias,and 50.7%and 57.3%lower annual mean RE,respectively;compared to the PWV-SC2 model,the annual mean RMSE and bias are reduced by 0.17 mm(20.5%)and 0.15 mm(71.4%),respectively,and the annual mean RE is reduced by 47.7%.Therefore,the MLP model has better accu-racy and adaptability and can be applied to high-precision PWV prediction in southern China.
GNSSatmospheric precipitable watermultilayer perceptronneural network modelregression modelaccuracy analysissouthern China
万方数据
维普
