Solar flares originate from active regions (ARs) hosting complex and strong bipolar magnetic fluxes. Forecasting the probability of an AR to flare and defining reliable precursors of intense flares, i.e., X- or M-class flares, are extremely challenging tasks in the space weather field. In this talk, we focus on two metrics as flare precursors, the unsigned flux R*, tested on MDI/SOHO data and calibrated for higher spatial resolution SDO/HMI maps, and a novel topological parameter D representing the complexity of a solar active region. The parameter D is based on the automatic recognition of magnetic polarity inversion lines (PILs) in identified SDO/HMI ARs and is able to evaluate their magnetic topological complexity. We use both a heuristic approach and a supervised machine-learning method to validate the effectiveness of these metrics to predict the occurrence of X- or M-class flares in a given solar AR during the following 24 hr period. Our feature ranking analysis shows that both parameters play a significant role in prediction performances. Moreover, the analysis demonstrates that the new topological parameter D is the only one, among 173 overall predictors, that is systematically ranked within the top 10 positions.
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