Operational flare forecasting aims at providing predictions that can be used to make decisions, typically on 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 network optimization are generated while accounting for the periodicity of the solar cycle. Specifically, this article 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-term memory network. The reliability of this approach is assessed in the case of two prediction windows containing the solar storms of March 2015, June 2015, and September 2017.

Operational solar flare forecasting via video-based deep learning

Marchetti F.;
2022

Abstract

Operational flare forecasting aims at providing predictions that can be used to make decisions, typically on 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 network optimization are generated while accounting for the periodicity of the solar cycle. Specifically, this article 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-term memory network. The reliability of this approach is assessed in the case of two prediction windows containing the solar storms of March 2015, June 2015, and September 2017.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3482300
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