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Machine learning for flare forecasting

Chapter
Publication Date:
2018
abstract:
This chapter describes the state-of-the-art concerning the use of machine learning methods for solar flare prediction. The general perspective is the one of the Flare Likelihood And Region Eruption foreCASTing (FLARECAST) project, which started in 2015 within the Horizon 2020 framework. The computational aspects of this project are described, with specific focus on the mathematical properties of the algorithms implemented in the FLARECAST pipeline and on the technological services that the project is providing to the heliophysics community.
Iris type:
02.01 Contributo in volume (Capitolo o Saggio)
Keywords:
Data clustering; Machine learning; Solar flare prediction; Solar flares; Supervised methods
List of contributors:
Piana, Michele; Massone, Annamaria
Handle:
https://iris.cnr.it/handle/20.500.14243/426551
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URL

http://www.scopus.com/inward/record.url?eid=2-s2.0-85070463084&partnerID=q2rCbXpz
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