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| Titel |
Time-dependent prediction degredation assessment of neural-networks-based TEC forecasting models |
| VerfasserIn |
Th. D. Xenos, S. S. Kouris, A. Casimiro |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 10, no. 6 ; Nr. 10, no. 6, S.585-587 |
| Datensatznummer |
250008214
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| Publikation (Nr.) |
 copernicus.org/npg-10-585-2003.pdf |
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| Zusammenfassung |
| 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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