Artículo

Autoprediction of Dst index using neural network techniques and relationship to the auroral geomagnetic indices

Stepanova, M. V.; Pérez, P.

Instituto de Geofísica, UNAM, publicado en Geofísica Internacional, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Entidad o dependencia
Instituto de Geofísica, UNAM
Revista
Repositorio
Contacto
Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

Cita

Stepanova, M. V., et al. (2000). Autoprediction of Dst index using neural network techniques and relationship to the auroral geomagnetic indices. Geofísica Internacional; Vol. 39 No. 1, 2000. Recuperado de https://repositorio.unam.mx/contenidos/42401

Descripción del recurso

Autor(es)
Stepanova, M. V.; Pérez, P.
Tipo
Artículo de Investigación
Área del conocimiento
Físico Matemáticas y Ciencias de la Tierra
Título
Autoprediction of Dst index using neural network techniques and relationship to the auroral geomagnetic indices
Fecha
2012-05-01
Resumen
The possibility of prediction of Dst variations using previous Dst values has been studied using a feedforward multi-layer perceptron. It was found that the Dst index can be autopredicted a few hours ahead. Both main and recovery phases of geomagnetic storms are accurately predicted up to 3 hours in advance. But, for more advanced predictions, a time shift between observed and predicted Dst minima is observed. The use of auroral electrojet indices as input has shown that there exists a slight relationship between these indices and Dst variation at least one hour ahead. Weak and moderate geomagnetic storms are predicted well, but the predicted Dst values for more intense storms are less negative than the observed minima, this may be related to the known saturation of auroral electrojet indices due to intense storm development. A prediction based on the PC index shows better correlation with Dst. Although the amplitude of Dst variation is not reproduced correctly, there is no time shift between measured and predicted location of Dst minima.
Tema
Dst; prediction; neural networks.; Dst; predicción; redes neuronales.
Idioma
spa
ISSN
0016-7169

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