Prediction of global positioning system total electron content using Neural Networks over South Africa |
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| Institution: | 1. Hermanus Magnetic Observatory, 7200 Hermanus, South Africa;2. Department of Physics and Electronics, Rhodes University, 6140 Grahamstown, South Africa;3. NASSP, University of Cape Town, 7201 Rondebosch, Cape Town, South Africa;1. Department of Geoinformatic Engineering, Inha University, 100 Inha-ro, Incheon 22212, South Korea;2. Department of ECE, KLEF, KL Deemed to be University, Guntur, India;1. Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India;2. Indian Institute of Geomagnetism, Mumbai 400005, India;1. Department of Space Physics, School of Electronic Information, Wuhan University, Wuhan, China;2. Beijing Institute of Applied Meteorology, Beijing, China;3. Shanghai Aircraft Design and Research Institute, Shanghai, China;4. Polar Research Institute of China, Shanghai, China;5. School of Physics and Electronic Engineering, Leshan Normal University, Leshan, China;1. Istituto Nazionale di Geofisica e Vulcanologia, Via di Vigna Murata 605, 00143 Rome, Italy;2. South African National Space Agency, Space Science, Hospital St, 7200 Hermanus, South Africa;3. Department of Physics and Electronics, Rhodes University, Artillery Road, 6140 Grahamstown, South Africa |
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| 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. |
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