Application of neural networks to South African GPS TEC modelling

Habarulema, John B; McKinnell, Lee-Anne; Cilliers, Pierre J; Opperman, Ben D L

Title: Application of neural networks to South African GPS TEC modelling
Creator: Habarulema, John B, McKinnell, Lee-Anne, Cilliers, Pierre J, Opperman, Ben D L
Date: 2009
Type: Article
Identifier: vital:6807
Identifier: http://hdl.handle.net/10962/d1004193
Identifier: http://dx.doi.org/10.1016/j.asr.2008.08.020
Description: The propagation of radio signals in the Earth’s atmosphere is dominantly affected by the ionosphere due to its dispersive nature. Global Positioning System (GPS) data provides relevant information that leads to the derivation of total electron content (TEC) which can be considered as the ionosphere’s measure of ionisation. This paper presents part of a feasibility study for the development of a Neural Network (NN) based model for the prediction of South African GPS derived TEC. The South African GPS receiver network is operated and maintained by the Chief Directorate Surveys and Mapping (CDSM) in Cape Town, South Africa. Vertical total electron content (VTEC) was calculated for four GPS receiver stations using the Adjusted Spherical Harmonic (ASHA) model. Factors that influence TEC were then identified and used to derive input parameters for the NN. The well established factors used are seasonal variation, diurnal variation, solar activity and magnetic activity. Comparison of diurnal predicted TEC values from both the NN model and the International Reference Ionosphere (IRI-2001) with GPS TEC revealed that the IRI provides more accurate predictions than the NN model during the spring equinoxes. However, on average the NN model predicts GPS TEC more accurately than the IRI model over the GPS locations considered within South Africa.
Language: English
Relation: Habarulema, John Bosco and McKinnell, L.-A. and Cilliers, P.J. and Opperman, B. D. L. (2009) Application of neural networks to South African GPS TEC modelling. Advances in Space Research, 43 (11). pp. 1711-1720. Available: http://dx.doi.org/10.1016/j.asr.2008.08.020

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McKinnell, Lee-Anne (Dr)

Habarulema, John Bosco (Dr)

RU Department of Physics and Electronics

SDG 09. Industry, Innovation and Infrastructure

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