Prediction of Ionospheric Total Electron Content Based on Causal Convolutional and LSTM Network
Siyu Tang, Zhi Huang
Abstract
The Total Electron Content (TEC) of the ionosphere is not only one of the key parameters to analyze the shape of the ionosphere, but also provides an important support for the navigation, positioning and other space applications to eliminate the additional ionospheric delay. Due to the temporal and spatial variation characteristics of ionospheric TEC, an ionospheric TEC hybrid deep learning model based on Causal convolution and Long Short-Term Memory network is proposed in this paper. The solar activity index <italic>F</italic><sub>10.7</sub>, the geomagnetic activity index<italic>Dst</italic> and the historical ionospheric TEC data are used as feature inputs to predict the TEC 24 hours in advance. Using CODE TEC data covering the low and high solar activities during 2005－2013, the performance of the model is comprehensively evaluated at Beijing station (40°N, 115°E), Wuhan station (30.53°N, 114.36°E) and Haikou station (20.02°N, 110.38°E). The results show the correlation coefficients between the predicted TEC values of the three stations and the actual values under different solar activity conditions are greater than 0.87, and most root mean square errors concentrated within 1 TECU. The prediction accuracy of the model is related to latitude, solar activity, geomagnetic activity and seasonal variation. Compared with the prediction model composed of LSTM network, the root mean square error of the proposed model is reduced by 15%, which provides a valuable reference for the practical application of the ionospheric TEC prediction.