Operational solar flare forecasting via video-based deep learning

TL;DR

This paper demonstrates that video-based deep learning, specifically a Long-term Recurrent Convolutional Network, can be effectively used for operational solar flare forecasting by accounting for solar cycle periodicity.

Contribution

It introduces an algorithm for creating balanced training sets aligned with solar cycle phases and applies a combined CNN-LSTM model for flare prediction.

Findings

Effective flare prediction for March 2015 and September 2017 storms.

Training set balancing improves model reliability.

Deep learning approach outperforms traditional methods.

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 storm of March 2015 and September 2017, respectively.