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Machine learning approaches for predicting the 10.7 cm radio flux from solar magnetogram data

Par Conseil national de recherches du Canada

DOITrouver le DOI : https://doi.org/10.1109/IJCNN.2019.8852332
AuteurRechercher : 1; Rechercher : ; Rechercher : 2
Affiliation
  1. Conseil national de recherches du Canada. Technologies numériques
  2. Conseil national de recherches du Canada. Herzberg en astronomie et en astrophysique
FormatTexte, Article
Conférence2019 International Joint Conference on Neural Networks (IJCNN), July 14-19, 2019, Budapest, Hungary
Sujetspace weather; solar magnetograms; solar radio flux; machine learning; intrinsic dimension; low dimensional space transformations; model trees; convolutional neural networks; ensemble models
Résumé
Using solar magetogram data, we explore potential of machine learning in space weather forecasting. In particular, unsupervised and supervised machine learning techniques are used to investigate the structure of magnetograms for 2006-2018, and their relation with the 10.7 cm solar radio flux. The similarity structure of the magnetograms is characterized with perception-based state of the art measures (the MSSIM index) and it was found that the data are contained in a space of intrinsically low dimension. The properties of these spaces were explored with methods preserving both local dissimilarity relationships, as well as conditional probability distributions within neighbourhoods. They reveal a clear relation with the intensity of the 10.7 cm flux. The flux was modeled using data driven supervised approaches in the form of model trees and convolutional neural networks. Models were found that allow prediction of the 10.7 cm radio flux with high accuracy. The results demonstrate significant potential which machine learning has in the space weather field.
Date de publication
Maison d’éditionIEEE
Dans
  • 2019 International Joint Conference on Neural Networks (IJCNN).
Langueanglais
Publications évaluées par des pairsOui
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Identificateur de l’enregistrement08c0dc22-3f4f-4284-85cc-24162f884018
Enregistrement créé2021-03-19
Enregistrement modifié2021-03-19
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