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Prediction of relativistic electron flux in the Earth’s outer radiation belt at geostationary orbit by adaptive methods

Myagkova, I. N. ; Dolenko, S. A. ; Efitorov, A. O. ; Shirokii, V. R. ; Sentemova, N. S.

Geomagnetism and Aeronomy, 2017, Vol.57 (1), p.8-15 [Periódico revisado por pares]

Moscow: Pleiades Publishing

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  • Título:
    Prediction of relativistic electron flux in the Earth’s outer radiation belt at geostationary orbit by adaptive methods
  • Autor: Myagkova, I. N. ; Dolenko, S. A. ; Efitorov, A. O. ; Shirokii, V. R. ; Sentemova, N. S.
  • Assuntos: Earth ; Earth and Environmental Science ; Earth Sciences ; Electrons ; Forecasting ; Geomagnetism ; Geophysics/Geodesy ; Group method of data handling ; Horizon ; Libration points ; Magnetic fields ; Mathematical models ; Neural networks ; Outer radiation belt ; Radiation ; Wind
  • É parte de: Geomagnetism and Aeronomy, 2017, Vol.57 (1), p.8-15
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: The paper investigates the possibilities of the prediction of the time series of the flux of relativistic electrons in the Earth’s outer radiation belt by parameters of the solar wind and the interplanetary magnetic field measured at the libration point and by the values of the geomagnetic indices. Different adaptive methods are used (namely, artificial neural networks, group method of data handling, and projection to latent structures). The comparison of quality indicators of predictions with a horizon of 1–12 h between each other and with the trivial model prediction has shown that the best result is obtained for the average value of the responses of three neural networks that have been trained with different sets of initial weights. The prediction result of the group method of data handling is close to the result of neural networks, and the projection to latent structures is much worse. It is shown that an increase in the prediction horizon from 1 to 12 h reduces its quality but not dramatically, which makes it possible to use these methods for medium-term prediction.
  • Editor: Moscow: Pleiades Publishing
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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