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Solar flares emanate from solar active regions hosting complex and strongbipolar magnetic fluxes. Estimating the probability of an active region toflare and defining reliable precursors of intense flares is an extremelychallenging task in the space weather field. In this work, we focus on twometrics as flare precursors, the unsigned flux R, tested on MDI/SOHO data andone of the most used parameters for flare forecasting applications, and a noveltopological parameter D representing the complexity of a solar active region.More in detail, we propose an algorithm for the computation of the R valuewhich exploits the higher spatial resolution of HMI maps. This algorithm leadsto a differently computed R value, whose functionality is tested on a set ofcycle 24th solar flares. Furthermore, we introduce a topological parameterbased on the automatic recognition of magnetic polarity-inversion lines inidentified active regions, and able to evaluate its magnetic topologicalcomplexity. We use both a heuristic approach and a supervised machine learningmethod to validate the effectiveness of these two descriptors to predict theoccurrence of X- or M- class flares in a given solar active region during thefollowing 24 hours period. Our feature ranking analysis shows that bothparameters play a significant role in prediction performances. Moreover, theanalysis demonstrates that the new topological parameter D is the only one,among 173 overall predictors, which is always present for all test subsets andis systematically ranked within the top-ten positions in all tests concerningthe computation of the weighs with which each predictor impacts the flareforecasting.

Flare Forecasting Algorithms Based on High-Gradient Polarity Inversion Lines in Active Regions