Prediction of Total Electron Content over South Africa Using Global Positioning System and Neural Networks
نویسندگان
چکیده
The Global Positioning System (GPS) makes it possible to study the dynamics of the ionosphere by supplementing ionospheric studies carried out using various techniques including ionosondes, incoherent scatter radars and low earth orbit satellites. Total electron content derived from GPS data (GPS TEC) is a key parameter characterising the ionosphere. Its time evolution provides an indication of ionospheric variability. Temporal and spatial total electron content (TEC) variations have significant effects on radio communications, applications involving navigational systems, GPS surveying and space weather. The analysis presented in this paper compared predicted Neural Network predictions of 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.
منابع مشابه
Nonlinear dynamic systems modeling using Gaussian processes: Predicting ionospheric total electron content over South Africa
[1] Two different implementations of Gaussian process (GP) models are proposed to estimate the vertical total electron content (TEC) from dual frequency Global Positioning System (GPS) measurements. The model falseness of GP and neural network models are compared using daily GPS TEC data from Sutherland, South Africa, and it is shown that the proposed GP models exhibit superior model falseness....
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