AUTHOR=Guastavino Sabrina , Marchetti Francesco , Benvenuto Federico , Campi Cristina , Piana Michele 

TITLE=Operational solar flare forecasting via video-based deep learning

JOURNAL=Frontiers in Astronomy and Space Sciences

VOLUME=Volume 9 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/astronomy-and-space-sciences/articles/10.3389/fspas.2022.1039805

DOI=10.3389/fspas.2022.1039805

ISSN=2296-987X

ABSTRACT=Operational flare forecasting aims at providing predictions that can be used to make decisions, typically at a daily scale, about the space weather impacts of flare occurrence. This study shows that video-based deep learning can be used for operational purposes when the training and validation sets used for the network optimization are generated while accounting for the periodicity of the solar cycle. Specifically, the paper describes an algorithm that can be applied to build up sets of active regions that are balanced according to the flare class rates associated to a specific cycle phase. These sets are used to train and validate a Long-term Recurrent Convolutional Network made of a combination of a convolutional neural network and a Long-Short Memory network. The reliability of this approach is assessed in the case of two prediction windows containing the solar storms of March and June 2015 and September 2017.