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Prediction of the Dst Geomagnetic Index Using Adaptive Methods

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Abstract

The potential is investigated of predicting the time series of the Dst geomagnetic index using various adaptive methods: artificial neural networks (classical multilayer perceptrons), decision trees (random forest), gradient boosting. The prediction is based on the parameters of the solar wind and interplanetary magnetic field measured at the Lagrange point L1 in the ACE spacecraft experiment. It is shown that the best prediction skill of the three adaptive methods is demonstrated by gradient boosting.

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Authors and Affiliations

  1. Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991, Moscow, Russia

    I. N. Myagkova, V. R. Shirokii, O. G. Barinov & S. A. Dolenko

  2. Lomonosov Moscow State University, GSP-1, 119991, Moscow, Russia

    R. D. Vladimirov

Authors
  1. I. N. Myagkova
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  2. V. R. Shirokii
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  3. R. D. Vladimirov
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  4. O. G. Barinov
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  5. S. A. Dolenko
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Correspondence to I. N. Myagkova.

Additional information

Translated from Meteorologiya i Gidrologiya, 2021, No. 3, pp. 38-46. https://doi.org/10.52002/0130-2906-2021-3-38-46.

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Myagkova, I.N., Shirokii, V.R., Vladimirov, R.D. et al. Prediction of the Dst Geomagnetic Index Using Adaptive Methods. Russ. Meteorol. Hydrol. 46, 157–162 (2021). https://doi.org/10.3103/S1068373921030031

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  • DOI: https://doi.org/10.3103/S1068373921030031

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