Comparison of daily solar flare peak flux forecast models based on regressive and neural network methods

We have developed a set of daily solar flare peak flux forecast models using the multiple linear regression (MLR), the auto regression (AR), and artificial neural network (ANN) methods. We consider input parameters as solar activity data from January 1996 to December 2013 such as sunspot area, X-ray flare peak flux, weighted total flux $T_F=1{\times}F_C+10{\times}F_M+100{\times}F_X$ of previous day, mean flare rates of a given McIntosh sunspot group (Zpc), and a Mount Wilson magnetic classification. We compute the hitting rate that is defined as the fraction of the events whose absolute differences between the observed and predicted flare fluxes in a logarithm scale are ${\leq}$ 0.5. The best three parameters related to the observed flare peak flux are as follows: weighted total flare flux of previous day (r=0.5), Mount Wilson magnetic classification (r=0.33), and McIntosh sunspot group (r=0.3). The hitting rates of flares stronger than the M5 class, which is regarded to be significant for space weather forecast, are as follows: 30% for the auto regression method and 69% for the neural network method.