Total Electron Content Prediction Model using the Artificial Neural Networks over the Eastern Africa Region

Emmanuel Sulungu, Christian Uiso


In this paper, development of a model using NN technique for prediction of GPS TEC over the Eastern Africa region is presented. TEC data was obtained from the Africa array and IGS network of ground based dual-frequency GPS receivers from 18 stations within the East African region. It covers approximately the area from ~2.6°N to ~26.9°S in magnetic latitudes and from ~95°E to ~112oE in magnetic longitudes. The input layer of the developed model consisted of seven neurons which were selected by considering the parameters that are known to affect the TECv data. The results showed that when the number of hidden layer neurons surpassed about 18, the RMSEs were noted to continuously increase indicating poor predictions beyond this number. The RMSE at this point was observed to be about 5.2 TECU which was lowest of all. The errors and relative errors were fairly small. Developed NN model estimated GPS TECv very well compared to IRI model. It is established in this study that, the IRI electron density at F2 peak (NmF2) gives good GPS TECv prediction when added as an input neuron to the NN.

Keywords: GPS; GPS TECv; Total Electron Content; Neural Network


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