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Adverse space weather effects can often be traced to solar flares, predictionof which has drawn significant research interests. The Helioseismic andMagnetic Imager (HMI) produces full-disk vector magnetograms with continuoushigh cadence, while flare prediction efforts utilizing this unprecedented datasource are still limited. Here we report results of flare prediction usingphysical parameters provided by the Space-weather HMI Active Region Patches(SHARP) and related data products. We survey X-ray flares occurred from 2010May to 2016 December, and categorize their source regions into four classes (B,C, M, and X) according to the maximum GOES magnitude of flares they generated.We then retrieve SHARP related parameters for each selected region at thebeginning of its flare date to build a database. Finally, we train amachine-learning algorithm, called random forest (RF), to predict theoccurrence of a certain class of flares in a given active region within 24hours, evaluate the classifier performance using the 10-fold cross validationscheme, and characterize the results using standard performance metrics.Compared to previous works, our experiments indicate that using the HMIparameters and RF is a valid method for flare forecasting with fairlyreasonable prediction performance. To our knowledge, this is the first timethat RF is used to make multi-class predictions of solar flares. We also findthat the total unsigned quantities of vertical current, current helicity, andflux near polarity inversion line are among the most important parameters forclassifying flaring regions into different classes.

Predicting Solar Flares Using SDO/HMI Vector Magnetic Data Product and Random Forest Algorithm