Browsing by Author "Yusuf, K.A."
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Item Geomagnetic storm effects on ionospheric total electron content at Toro, Nigeria(Science Forum (Journal of Pure and Applied Sciences), 2022-12-31) Ige, S.O.; Agbana, S.A.; Bello, S.A.; Yusuf, K.A.Energy input into the upper atmosphere during geomagnetic storms takes the form of enhanced electric fields, currents, and energetic particle precipitation. This storm-time energy input affects the ionosphere–thermosphere coupling which leads to the balance between the storm-induced circulation and the regular circulation and determines the spatial distribution of negative and positive storm effects. This study revealed episodes of positive and negative ionospheric storm effects during these storm events with positive effects dominating the main phases at the Toro, Nigeria International Global Navigation System Satellites Service network station while the recovery period is characterized by either positive or negative effects or a combination of both. The geomagnetic storm time was determined using the geomagnetic Dst index, southward interplanetary magnetic field, and auroral electrojet index. The development of the positive ionospheric storm effect at the low latitudes is caused by the upward movement of ionization to regions of reduced recombination rate.Item Performance Estimation of Neural Network TEC Prediction Models over Toro Station(National Institute of Physics, 2022-12) Bello, S.A.; Orisatuyi, M.J.; Yusuf, K.A.; Shehu, S.J.; Oyinkanola, L.O.A.; Ige, S.O.; Lawal, S.K.; Oladipo, M.This paper presents the prediction of hourly Total Electron Content (TEC) obtained from a Global navigation satellite system (GNNS) receiver at Toro station (10.12°N, 9.12°E), Bauchi, Nigeria and developed an ionospheric model using a neural network (NN) by utilizing the TEC data. The studied period is based on the available data during the period from 2014 to 2016. Four neural network configurations with different inputs which include the day number, hour number, sunspot number (SSN) and solar radio flux (F10.7) were used. Each configuration was trained with TEC data between the years 2014 to 2016. The best neural network used for prediction had the least mean squared error (MSE) of 8.68 TECU and root mean squared error (RMSE) value of 2.95 TECU. The comparison was made between TEC from the observatory station and predicted TEC from the best neural network (NN) model. The developed NN model was used to predict some selected days that fall between the four astronomical seasons. The results show that the model performed well on the 17th of March 2014 with an MSE of 12.35 TECU and an RMSE value of 3.11 TECU.