  We develop forecast models of daily probability of major flares (M- and
X-class) based on empirical relationships between photospheric magnetic
parameters and daily flaring rates from May 2010 to April 2018. In this study,
we consider ten magnetic parameters characterizing size, distribution, and
non-potentiality of vector magnetic fields from Solar Dynamics Observatory
(SDO)/Helioseismic and Magnetic Imager (HMI) and Geostationary Operational
Environmental Satellites (GOES) X-ray flare data. The magnetic parameters are
classified into three types: the total unsigned parameters, the total signed
parameters, and the mean parameters. We divide the data into two sets
chronologically: 70% for training and 30% for testing. The empirical
relationships between the parameters and flaring rates are used to predict
flare occurrence probabilities for a given magnetic parameter value. Major
results of this study are as follows. First, major flare occurrence rates are
well correlated with ten parameters having correlation coefficients above 0.85.
Second, logarithmic values of flaring rates are well approximated by linear
equations. Third, the total unsigned and signed parameters achieved better
performance for predicting flares than the mean parameters in terms of
verification measures of probabilistic and converted binary forecasts. We
conclude that the total quantity of non-potentiality of magnetic fields is
crucial for flare forecasting among the magnetic parameters considered in this
study. When this model is applied for operational use, this model can be used
using the data of 21:00 TAI with a slight underestimation of 2 - 6.3%.
