Browsing by Author "Oladipo, M."

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    Effect of solar wind pressure on geomagnetic northward component over some selected low-latitude African stations
    (FUW Trends in Science & Technology Journal, 2023-03-18) Bello, S.A.; Yusuf, K. A.; Agbaje, P.; Shehu, S. J.; Lawal, S. K.; Oyinkanola, L. O. A.; Oladipo, M.
    The geomagnetic field is a magnetic field that extends into space, interacting with the solar wind, a stream of charged particles emanating from the sun. The geomagnetic (H) component data used for this project were obtained from Magnetic Data Acquisition System (MAGDAS) magnetometer at five different stations in Africa covering magnetic latitudes (MLAT) from 21.13⁰ in the northern hemisphere to -39.21⁰ in the southern hemisphere and magnetic longitude (MLON) between 69° to 120° The stations are Fayum, Egypt (21.13° MLAT, 102.38° MLON), Ilorin, Nigeria (-1.82° MLAT, 76.80° MLON), Hermanus, South Africa (-42.29° MLAT, 82.20° MLON), Dal Es Salaam, Tanzania (-16.26° MLAT, 110.59° MLON), Abidjan, Ivory Coast (6.32° MLAT, 69.23° MLON). The daily variation of the geomagnetic H component (ΔH) is calculated by subtracting the baseline values. The baseline value is the average value of the nighttime flanking hours. The study examines the effect of solar wind pressure on geomagnetic fields across different hemispheres at different seasons. The outcomes of this study demonstrate geomagnetic disturbances of hemispheric asymmetry which varies with seasons for some selected African stations. After a close study of low-latitude geomagnetic disturbances caused by solar wind pressure enhancements, it is found that there is a significant hemispheric asymmetry of the geomagnetic disturbances and that this hemispheric asymmetry depends on the season and interplanetary magnetic field (IMF) orientation.
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    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.