PRESOL: a web-based computational setting for feature-based flare forecasting

• URL: http://arxiv.org/abs/2510.01799v1
• Date: Thu, 02 Oct 2025 08:40:10 GMT
• Title: PRESOL: a web-based computational setting for feature-based flare forecasting
• Abstract summary: Solar flares are the most explosive phenomena in the solar system and the main trigger of the events' chain.<n>Data-driven solar flare forecasting relies on either deep learning approaches or machine learning algorithms.<n>This paper describes a web-based technological platform for the execution of a computational pipeline of feature-based machine learning methods.
• Score: 1.759008116536278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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