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We study the predictive capabilities of magnetic feature properties (MF)generated by Solar Monitor Active Region Tracker (SMART) for solar flareforecasting from two datasets: the full dataset of SMART detections from 1996to 2010 that has been previously studied by Ahmed et al. (2011) and a subset ofthat dataset which only includes detections that are NOAA active regions (ARs).Main contributions: we use marginal relevance as a filter feature selectionmethod to identify most useful SMART MF properties for separating flaring fromnon-flaring detections and logistic regression to derive classification rulesto predict future observations. For comparison, we employ a Random Forest,Support Vector Machine and a set of Deep Neural Network models, as well asLasso for feature selection. Using the linear model with three features weobtain significantly better results (TSS=0.84) to those reported by Ahmed etal.(2011) for the full dataset of SMART detections. The same model producedcompetitive results (TSS=0.67) for the dataset of SMART detections that areNOAA ARs which can be compared to a broader section of flare forecastingliterature. We show that more complex models are not required for this data.

Solar flare forecasting from magnetic feature properties generated by Solar Monitor Active Region Tracker