Over the past 10 years, a paradigm shift has happened in the world of science and
information technology. Open science is becoming the de facto standard, as underlined by …

Abstract The use of Deep Learning models to forecast geomagnetic storms is achieving
great results. However, the evaluation of these models is mainly supported on generic …

Abstract Machine learning (ML) has been increasingly applied to space weather and
ionosphere problems in recent years, with the goal of improving modeling and forecasting …

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 addresses the prediction of geomagnetic disturbances by exploiting machine
learning techniques. Specifically, the Long-Short Term Memory recurrent neural network …

The Dst index is the geomagnetic storm index used to measure the energy level of
geomagnetic storms, and the prediction of this index is of great significance for geomagnetic …

We introduce a new framework called Machine Learning (ML) based Auroral Ionospheric
electrodynamics Model (ML‐AIM). ML‐AIM solves a current continuity equation by utilizing …

Detecting large-scale flux ropes (FRs) embedded in interplanetary coronal mass ejections
(ICMEs) and assessing their geoeffectiveness are essential since they can drive severe …

In many contexts, customized and weighted classification scores are designed in order to
evaluate the goodness of the predictions carried out by neural networks. However, there …

The Dst index is the geomagnetic storm index used to measure the energy level of
geomagnetic storms, and the prediction of this index is of great significance for the …