Browsing by Author "Bello, S.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 Geophysical Investigation of Groundwater Contamination in a Solid Waste Disposal Site(Al-Hikmah Journal of Pure & Applied Sciences, 2017-07-26) Lawal, T.O.; Orosun, M.M.; Ige, S.O.; Sunday, J.A.; Shehu, A.T.; Bello, S.A.; Nwankwo, L.I.A geophysical investigation of groundwater contamination within the solid waste disposal site was carried out at Tanke Tipper Garage, a typical non-controlled open dumpsite located in Ilorin, Nigeria. The aim of the investigation is to study the spatial distribution of contaminant plumes in the groundwater and ascertain if areas that can be free from contaminants are available in the study area. In view of this, four (4) vertical electrical soundings (VES) methods employing Half Schlumberger electrode array were conducted due to limited space in the area with maximum electrode spacing of 50 m. The field data were processed and Interpreted Resistivities Values were obtained by iterative computer modeling of the apparent resistivity data. The interpreted resistivity value showed that contaminant plumes at low zones with values ranging between 30.2 and 81.5 ohm-m extending from the surface down to the aquifer of shallow groundwater of less than 15 m. The hydraulic conductivity of the subsurface layers of interpreted VES points was also calculated and values ranging between 0.076 m/s and 0.418 m/s were obtained respectively. In order to complement the result of geophysical data, a physiochemical analysis of water samples from the existing hand dug well within the premises of the dumpsite was also conducted and high values of measured parameters were observed. This is an indication of groundwater contamination resulting from the solid waste leachate accretion found within the shallow aquifer zones.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.