Study on the NeuralProphet forecast TEC model over China during the severe geomagnetic storm in March 2015

Ionospheric delay is one of the significant error sources in global navigation satellite system. It is essential to improve the accuracy of ionospheric TEC modeling and forecasting for enhancing the accuracy of satellite navigation positioning. In this paper, a NeuralProphet neural network model (NP) is constructed with solar radiation flux index (F10.7), geomagnetic activity index (Dst), geographic coordinates and GIM from Chinese Academy of Sciences (CAS) as influence factors and input parameters. And the presented NP model is applied for the short-term forcasting of ionospheric TEC over China during the severe magnetic storm in March 2015. In order to verify the performence of NP model, a Long Short-term Memory Neural Network (LSTM) model is implemented for comparative analysis. The statistical analysis results show that the root mean square error (RMSE) and relative deviation (RD) of NP model during the geomagnetic storm period (DOY076-078) are 0. 83 TECU and 3. 13%, respectively, which are 1. 49 TECU and 10. 25% more accurate than LSTM model from the perspectives of absolute and relative accuracy. And for the ratio of RMSE less than 1. 5 TECU, NP forecast model is about 97. 24%, which is much better than LSTM model. The TEC predictions from NP model has good consistency and unbiasedness with CAS-TEC showing the mean bias of 0. 01 TECU, while the LSTM model has a larger mean bias of 1. 49 TECU. The RMSE of NP model are 1. 12, 0. 83 and 0. 44 TECU, respectively, from low to mid-latitudinal zone, and the forecasting accuracy is 1. 94, 1. 56 and 1. 23 TECU higher than LSTM model. The proposed NP forecast model has a significantly better forecasting performance than LSTM, which would be useful for characterizing the spatial temporal characteristics more accurately under disturbed conditions over China.