  Prediction of solar flares is an important task in solar physics. The
occurrence of solar flares is highly dependent on the structure and the
topology of solar magnetic fields. A new method for predicting large (M and X
class) flares is presented, which uses machine learning methods applied to the
Zernike moments of magnetograms observed by the Helioseismic and Magnetic
Imager (HMI) onboard the Solar Dynamics Observatory (SDO) for a period of six
years from 2 June 2010 to 1 August 2016. Magnetic field images consisting of
the radial component of the magnetic field are converted to finite sets of
Zernike moments and fed to the Support Vector Machine (SVM) classifier. Zernike
moments have the capability to elicit unique features from any 2-D image, which
may allow more accurate classification. The results indicate whether an
arbitrary active region has the potential to produce at least one large flare.
We show that the majority of large flares can be predicted within 48 hours
before their occurrence, with only 10 false negatives out of 385 flaring active
region magnetograms, and 21 false positives out of 179 non-flaring active
region magnetograms. Our method may provide a useful tool for prediction of
solar flares which can be employed alongside other forecasting methods.
