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Total electron content (TEC) forecasting by Cascade Modeling: A possible alternative to the IRI‐2001
Author(s) -
Senalp Erdem Turker,
Tulunay Ersin,
Tulunay Yurdanur
Publication year - 2008
Publication title -
radio science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 84
eISSN - 1944-799X
pISSN - 0048-6604
DOI - 10.1029/2007rs003719
Subject(s) - tec , total electron content , cascade , artificial neural network , space weather , ionosphere , computer science , meteorology , chemistry , physics , artificial intelligence , geophysics , chromatography
The ionospheric parameter, total electron content (TEC) is one of the key parameters in navigation and telecommunication applications. A small group at METU in Ankara has been developing data driven models in order to forecast the ionospheric parameters since 1990. In particular, results on forecasting TEC values one hour in advance by using their Neural Network Model—METU‐NN have been reported previously. Since then, some work has been done in order to increase the performance of the METU‐NN. In this paper, the most recent advanced model of the Neural Network containing Cascade Modeling (METU‐NN‐C) based on the Hammerstein systems is introduced. To demonstrate the performance of the METU‐NN‐C model, the Chilbolton and the Hailsham TEC values are considered during severe Space Weather conditions in some periods of 2001 and 2002. The authors have considered the METU‐NN‐C model as an alternative to the IRI‐2001. In order to facilitate a comparison, IRI‐2001 Hailsham TEC values are compared with those of the METU‐NN‐C by taking observed values as a basis.