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The ionosphere model is essential to satellite-based systems to accurately correct the ionospheric error encountered by satellite signals en route. The Levenberg-Marquardt backpropagation (LMBP) algorithm in the artificial neural network (ANN) was used in this work to predict the total electron content (TEC) within the trough of equatorial ionization anomaly (EIA) over Nigeria. Two sets of data were used over the period of three consecutive years (2011-2013) of high solar activity. The first set was used as an input to the ANN model and the second set of data was used as a target. Seventy percent of the data sets were used to train the network, 15% of the data were used for validation, and 15% used for testing. The performance of the model was assessed during specific quiet and disturbed geomagnetic conditions. The regression analysis of the model output was optimized by minimizing a cost function of the mean square error (MSE). The results of the errors, regression, and comparative analyses have revealed that the ANN model is able to predict accurate and reliable TEC that compares well with the actual experimental data at any geophysical conditions. Hence, this model would be useful to forecast TEC over Nigeria to a reliable threshold. Key words: LMBP-ANN, TEC, input, EIA, space weather. Un modele de l'ionosphere est essentiel dans un systeme base sur l'utilisation des satellites, afin de corriger de facon precise l'erreur ionospherique de la trajectoire du signal satellitaire. L'algorithme de retropropagation de LevenbergMarquardt (RPLM/LMBP dans un reseau neuronal artificiel (RNA/ANN) est le modele utilise ici pour predire le contenu electronique total (CET/TEC) dans la depression de l'anomalie d'ionisation equatoriale (AEI/EIA) au-dessus du Nigeria. Deux ensembles de donnees sont utilisees sur la periode de trois annees consecutives (2011-2013) de haute activite solaire. Le premier ensemble a ete utilise comme donnee d'entree pour le RNA et le second comme cible. 70 % des ensembles de donnees servent pour entrainer le reseau, 15 % pour le valider et 15 % pour le tester. La validite du modele est verifiee pendant des periodes de conditions geomagnetiques tranquilles et actives. L'analyse par regression des resultats du modele est optimisee en minimisant une fonction de cout de l'erreur quadratique moyenne (EQM/MSE). Le resultat pour les erreurs et les analyses comparatives montrent que le modele RNA est capable de predictions du CET precises et fiables, qui se comparent bien avec les valeurs experimentales actuelles pour toute condition geomagnetique. Ainsi, ce modele serait tres utile pour predire le CET au-dessus du Nigeria avec un bon niveau de fiabilite. [Traduit par la Redaction] Mots-cles : RPLM-RNA/LMBP-ANN, CET/TEC, entree, AEI/EIA, meteo spatiale., 1. Introduction The ionosphere is a region of the upper atmosphere extending from approximately 50 km to about 2000 km above the Earth's surface. This region contains amounts of electrons [...]

This paper examines the accuracy of the Global Positioning System (GPS) within the African low-latitude sector. We evaluated the vertical and horizontal of the single-point positioning (SPP) accuracy of GPS and critically analysed the effects of the equatorial ionospheric anomaly (EIA) over the region of study. Our results imply that using single-frequency GPS for any application could give a positioning error up to 45.00 m vertically and ~25.00 m horizontally. The study revealed that 54% of GNSS positioning errors during the night-time could be linked to ionospheric plasma irregularities. Also, positioning errors are higher in western Africa than in the eastern region. The influence of geomagnetic activities is not consistent with GPS positioning accuracy. However, positioning errors are lower during geomagnetically disturbed conditions in comparison to quiet conditions. The unique phenomena of the EIA can severely limit GPS services at night-time for positioning in the study area. [ABSTRACT FROM AUTHOR]