Machine Learning-Based Short-Term GPS TEC Forecasting During High Solar Activity and Magnetic Storm Periods

Abstract

Precise ionospheric total electron content (TEC) is critical for many aerospace applications, and forecasting ionospheric TEC is of great significance to it. Besides, short-term prediction of TEC values fills the gap between the TEC product latency and the precision. The machine learning-based approaches are promising in solving the nonlinear prediction issues, particularly suitable for short-term global positioning system TEC forecasting due to its complex temporal and spatial variation. In this article, four different machine learning models, i.e., artificial neural network, long short-term memory networks, adaptive neuro-fuzzy inference system based on subtractive clustering, and gradient boosting decision tree (GBDT) are applied for forecasting ionospheric TEC in three IGS GNSS monitoring stations at the low-latitude region (16°S to 10°S). The performance of these approaches in extreme conditions is investigated, including the high solar activity and magnetic storm, which are the most challenging scenario for TEC prediction. The results show that the machine learning algorithms outperform the global ionospheric map prediction model. The prediction accuracy during the high solar activity period was improved from 37.93% to 49.28%. During the magnetic storm period, the prediction accuracy was improved from 28.16% to 67.39%. Among the machine learning algorithms, the GBDT model outperforms the rest three algorithms in ionosphere prediction scenarios, which improves the prediction accuracy by 5.6% and 12.7% than the rest three approaches on average during high solar activity (2012–2015) and magnetic storm periods respectively.

Keywords

• Gradient boosting decision tree (GBDT)
• ionosphere prediction
• machine learning
• neural network (NN)
• total electron content (TEC)