# Operational solar flare forecasting via video-based deep learning

arXiv: https://arxiv.org/abs/2209.05128 · HTML: https://mlsanity.com/paper/2209.05128/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 12 Sep 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Predicting Solar Flares Using a Long Short-Term Memory Network](https://mlsanity.com/paper/1905.07095/) (2019)
- [Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting](https://mlsanity.com/paper/2209.07406/) (2022)
- [Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data](https://mlsanity.com/paper/2204.03710/) (2022)
- [Score-oriented loss (SOL) functions](https://mlsanity.com/paper/2103.15522/) (2021)
- [Prediction of severe thunderstorm events with ensemble deep learning and radar data](https://mlsanity.com/paper/2109.09791/) (2021)
- [Bad and good errors: value-weighted skill scores in deep ensemble learning](https://mlsanity.com/paper/2103.02881/) (2021)
- [Machine learning as a flaring storm warning machine: Was a warning machine for the September 2017 solar flaring storm possible?](https://mlsanity.com/paper/2007.02425/) (2020)
- [Desaturating EUV observations of solar flaring storms](https://mlsanity.com/paper/1904.04211/) (2019)

## Built on by (most cited first, 5 of 5)

- [Physics-driven machine learning for the prediction of coronal mass ejections' travel times](https://mlsanity.com/paper/2305.10057/) (2023)
- [A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics](https://mlsanity.com/paper/2305.13472/) (2023)
- [Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches](https://mlsanity.com/paper/2403.09847/) (2024)
- [Deep Learning for Active Region Classification: A Systematic Study from Convolutional Neural Networks to Vision Transformers](https://mlsanity.com/paper/2410.17816/) (2024)
- [The Multiclass Score-Oriented Loss (MultiSOL) on the Simplex](https://mlsanity.com/paper/2511.22587/) (2025)

## Abstract (arXiv)

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.