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| Titel |
Prediction of geomagnetic storms from solar wind data with the use of a neural network |
| VerfasserIn |
H. Lundstedt, P. Wintoft |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
0992-7689
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| Digitales Dokument |
URL |
| Erschienen |
In: Annales Geophysicae ; 12, no. 1 ; Nr. 12, no. 1, S.19-24 |
| Datensatznummer |
250010390
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| Publikation (Nr.) |
 copernicus.org/angeo-12-19-1994.pdf |
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| Zusammenfassung |
| An artificial feed-forward neural network
with one hidden layer and error back-propagation learning is used to predict the
geomagnetic activity index (Dst) one hour in advance. The Bz-component
and ΣBz, the density, and the velocity of the
solar wind are used as input to the network. The network is trained on data
covering a total of 8700 h, extracted from the 25-year period from 1963 to 1987,
taken from the NSSDC data base. The performance of the network is examined with
test data, not included in the training set, which covers 386 h and includes
four different storms. Whilst the network predicts the initial and main phase
well, the recovery phase is not modelled correctly, implying that a single
hidden layer error back-propagation network is not enough, if the measured Dst
is not available instantaneously. The performance of the network is independent
of whether the raw parameters are used, or the electric field and square root of
the dynamical pressure. |
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