Modeling of Ionospheric Response to Geomagnetic Storms over the East African Low Latitude Region Using Artificial Neural Networks — Oak Academic Publishing
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Modeling of Ionospheric Response to Geomagnetic Storms over the East African Low Latitude Region Using Artificial Neural Networks
Department of Physics, Mbarara University of Science and Technology, Mbarara, Uganda
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Department of Physics, Mbarara University of Science and Technology, Mbarara, Uganda
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Department of Physics, Mbarara University of Science and Technology, Mbarara, Uganda
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Department of Physics, Mbarara University of Science and Technology, Mbarara, Uganda
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Department of Nursing, Mbarara University of Science and Technology, Mbarara, Uganda
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Department of Biomedical Engineering, Mbarara University of Science and Technology, Mbarara, Uganda
1 Department of Physics, Mbarara University of Science and Technology, Mbarara, Uganda
2 Department of Physics, Mbarara University of Science and Technology, Mbarara, Uganda
3 Department of Physics, Mbarara University of Science and Technology, Mbarara, Uganda
4 Department of Physics, Mbarara University of Science and Technology, Mbarara, Uganda
5 Department of Nursing, Mbarara University of Science and Technology, Mbarara, Uganda
6 Department of Biomedical Engineering, Mbarara University of Science and Technology, Mbarara, Uganda
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 ) ≤ − 30 nT 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 10.7 P ), 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.
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