  Solar flares, especially the M- and X-class flares, are often associated with
coronal mass ejections (CMEs). They are the most important sources of space
weather effects, that can severely impact the near-Earth environment. Thus it
is essential to forecast flares (especially the M-and X-class ones) to mitigate
their destructive and hazardous consequences. Here, we introduce several
statistical and Machine Learning approaches to the prediction of the AR's Flare
Index (FI) that quantifies the flare productivity of an AR by taking into
account the numbers of different class flares within a certain time interval.
Specifically, our sample includes 563 ARs appeared on solar disk from May 2010
to Dec 2017. The 25 magnetic parameters, provided by the Space-weather HMI
Active Region Patches (SHARP) from Helioseismic and Magnetic Imager (HMI) on
board the Solar Dynamics Observatory (SDO), characterize coronal magnetic
energy stored in ARs by proxy and are used as the predictors. We investigate
the relationship between these SHARP parameters and the FI of ARs with a
machine-learning algorithm (spline regression) and the resampling method
(Synthetic Minority Over-Sampling Technique for Regression with Gaussian Noise,
short by SMOGN). Based on the established relationship, we are able to predict
the value of FIs for a given AR within the next 1-day period. Compared with
other 4 popular machine learning algorithms, our methods improve the accuracy
of FI prediction, especially for large FI. In addition, we sort the importance
of SHARP parameters by Borda Count method calculated from the ranks that are
rendered by 9 different machine learning methods.
