Prediction of global positioning system total electron content using Neural Networks over South Africa

Abstract

Global positioning system (GPS) networks have provided an opportunity to study the dynamics and continuous changes in the ionosphere by supplementing ionospheric studies carried out using various techniques including ionosondes, incoherent scatter radars and satellites. Total electron content (TEC) is one of the physical quantities that can be derived from GPS data, and provides an indication of ionospheric variability. This paper presents a feasibility study for the development of a Neural Network (NN) based model for the prediction of South African GPS derived TEC. Three South African locations were identified and used in the development of an input space and NN architecture for the model. The input space included the day number (seasonal variation), hour (diurnal variation), Sunspot Number (measure of the solar activity), and magnetic index (measure of the magnetic activity). An analysis was done by comparing predicted NN TEC with TEC values from the IRI-2001 version of the International Reference Ionosphere (IRI), validating GPS TEC with ionosonde TEC (ITEC) and assessing the performance of the NN model during equinoxes and solstices. For this feasibility model, GPS TEC was derived for a limited number of years using an algorithm still in the early phases of validation. However, results show that NNs predict GPS TEC more accurately than the IRI at South African GPS locations, but that more good quality GPS data is required before a truly representative empirical GPS TEC model can be released.