TITLE:
Modeling of Ionospheric Response to Geomagnetic Storms over the East African Low Latitude Region Using Artificial Neural Networks
AUTHORS:
Vaola Agaba, Valence Habyarimana, Sharon Aol, Tom Mutabazi, Vallence Niyonzima, Eugene Bizimana
KEYWORDS:
Geomagnetic Storms, Ionospheric Response, Total Electron Content (TEC), Space Weather, Ionospheric Modeling, Artificial Neural Networks (ANNs)
JOURNAL NAME:
Atmospheric and Climate Sciences,
Vol.15 No.4,
October
14,
2025
ABSTRACT: Geomagnetic storms significantly disturb the ionosphere, impacting satellite-based systems such as the Global Navigation Satellite System (GNSS), communication links, and power infrastructure. This study models the ionospheric response to geomagnetic storms over East Africa using GNSS-derived Total Electron Content (TEC) data from five International GNSS Service (IGS) stations during solar cycle 24 (2008-2019). We identified geomagnetic storms using the criteria of Disturbance storm time
(
Dst
)≤−30nT
and
kp≥3
, yielding 802 events, of which 787 were CIR-driven and 15 CME-driven. To determine the optimal background method for ionospheric storm extraction, five approaches were tested. The monthly median vertical TEC (VTEC) method provided the best performance (Root mean square error, RMSE = 26.42 TECU; Mean absolute error, MAE = 17.10 TECU), while the five internationally quietest days gave the least performance (RMSE = 50.82 TECU; MAE = 30.96 TECU). We then developed a storm-time ARTIFICIAL neural Network (ANN) model for ionospheric storms. The inputs include solar activity factor (
F
10.7P
), hour of day (HR), day of year (DOY), latitude, longitude, z-component of the interplanetary magnetic field (IMF Bz), and Dst index, representing solar, diurnal, seasonal, spatial, and geomagnetic dependencies. The output was ΔVTEC, with storm conditions defined as deviations with a magnitude greater than 45%. The optimum ANN model had a configuration of 9 inputs, 16 hidden neurons, 1 output, with an RMSE of 23.49%. The ANN model performance was robust under high solar activity and quiet to moderate geomagnetic conditions with an average RMSE of 23% and MAE of 16.5%, though errors increased during intense geomagnetic storm periods. These results demonstrate that ANN models can reliably capture diurnal and seasonal ionospheric variability in East Africa and provide a foundation for regional space weather forecasting and mitigation strategies.