PRESOL: a web-based computational setting for feature-based flare forecasting
This work addresses the need for explainable and operational flare forecasting tools for space weather researchers and operators, though it is incremental as it builds on existing machine learning approaches.
The authors tackled the problem of solar flare forecasting by developing a web-based platform that executes a computational pipeline of feature-based machine learning methods, providing predictions, feature ranking, and performance assessment.
Solar flares are the most explosive phenomena in the solar system and the main trigger of the events' chain that starts from Coronal Mass Ejections and leads to geomagnetic storms with possible impacts on the infrastructures at Earth. Data-driven solar flare forecasting relies on either deep learning approaches, which are operationally promising but with a low explainability degree, or machine learning algorithms, which can provide information on the physical descriptors that mostly impact the prediction. This paper describes a web-based technological platform for the execution of a computational pipeline of feature-based machine learning methods that provide predictions of the flare occurrence, feature ranking information, and assessment of the prediction performances.