This paper reviews the application of artificial intelligence methods for space weather prediction, with a focus on solar flare forecasting. It contrasts physics-based and data-driven approaches, highlighting how machine learning models trained on large datasets of solar magnetograms and flare records can provide probabilistic predictions. While AI methods represent the current state-of-the-art, challenges remain in terms of data imbalance, feature selection, and model interpretability.
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