| Volumes and Issues Contents of Issue 6 |
www.nonlin-processes-geophys.net/10/585/2003/
© Author(s) 2003. This work is licensed
under a Creative Commons License.
Time-dependent prediction degredation assessment of neural-networks-based TEC forecasting models
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.
Full Article (PDF, 96 KB)
Citation: Xenos, Th. D., Kouris, S. S., and Casimiro, A.: Time-dependent prediction degredation assessment of neural-networks-based TEC forecasting models, Nonlin. Processes Geophys., 10, 585-587, 2003. Bibtex EndNote Reference Manager
