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TitleMachine learning approaches for predicting the 10.7 cm radio flux from solar magnetogram data
AuthorValdés, J J; Nikolic, L; Tapping, K
Source2019 International Joint Conference on Neural Networks (IJCNN), proceedings; N-19557, 2019 p. 1-8,
Alt SeriesNatural Resources Canada, Contribution Series 20180438
MeetingIJCNN 2019 - International Joint Conference on Neural Networks; Budapest; HU; July 14-19, 2019
Mediaon-line; digital
File formatpdf (Adobe® Reader®); html
Subjectsgeophysics; Science and Technology; Nature and Environment; solar variations; magnetic field; models; Methodology; Forecasting; machine learning
Illustrationssketch maps; time series; tables; 3-D images; plots
ProgramPublic Safety Geoscience Northern Canada Geohazards Project
Released2019 09 30
AbstractUsing solar magnetogram 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.
Summary(Plain Language Summary, not published)
Space weather refers to the dynamic conditions on the Sun and in the space environment, in particular, in the near-Earth environment, that can affect critical infrastructure. NRCan operates the Canadian Space Weather Forecast Centre and conducts research into space weather effects on power systems, pipelines, radio communications and GNSS positioning to help Canadian industry understand and mitigate the effects of space weather. In this work we investigate potential of using machine learning approaches to forecast space weather. Using solar magnetograms, we demonstrate forecast of the solar radio flux.

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