Abstract：Considering that ionospheric total electron content(TEC) presents as non-stationary, nonlinear and high noise characteristics, we use ionospheric TEC data provided by international GPS service(IGS) to predict 3 days TEC using the back propagation(BP) neural network model and the ARMA model. We compare the prediction performance and accuracy of each model in different seasons, different ionospheric active intensity and different sample lengths. The results show that the two models can well reflect the change characteristics of TEC in different seasons, among which ARMA model is slightly better than BP neural network in spring and winter. In the quiet period, the average relative accuracy of the two models is 87.3 % (BP) and 87.5% (ARMA), which means the predictive effect is similar. In the active period, the average relative accuracy of the two models is 78.5% (BP) and 75.5% (ARMA). The accuracy of BP neural network is 3% higher than that of ARMA model. With the increase of sample length, the accuracy of BP neural network model reaches the maximum on the 21st day, the prediction accuracy of ARMA model decreases with the increase of sample length.
HUANG Wenxi,ZHU Fuying,ZHAI Dulin et al. Comparative Analysis of BP Neural Network and ARMA Model in Short-Term Prediction of Mid-Latitude TEC[J]. jgg, 2021, 41(3): 262-267.
