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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 6
Nonlin. Processes Geophys., 10, 585–587, 2003
https://doi.org/10.5194/npg-10-585-2003
© Author(s) 2003. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Nonlin. Processes Geophys., 10, 585–587, 2003
https://doi.org/10.5194/npg-10-585-2003
© Author(s) 2003. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  31 Dec 2003

31 Dec 2003

Time-dependent prediction degredation assessment of neural-networks-based TEC forecasting models

Th. D. Xenos1, S. S. Kouris1, and A. Casimiro2 Th. D. Xenos et al.
  • 1Department of Electrical and Comp. Eng., Univ. of Thessaloniki, Greece
  • 2Department of Electrical and Comp. Eng., Univ. do Algarve, Faro, Portugal

Abstract. An estimation of the difference in TEC prediction accuracy achieved when the prediction varies from 1 h to 7 days in advance is described using classical neural networks. Hourly-daily Faraday-rotation derived TEC measurements from Florence are used. It is shown that the prediction accuracy for the examined dataset, though degrading when time span increases, is always high. In fact, when a relative prediction error margin of ± 10% is considered, the population percentage included therein is almost always well above the 55%. It is found that the results are highly dependent on season and the dataset wealth, whereas they highly depend on the foF2 - TEC variability difference and on hysteresis-like effect between these two ionospheric characteristics.

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