http://arxiv.org/abs/1812.02652
We study the predictive capabilities of magnetic feature properties (MF) generated by Solar Monitor Active Region Tracker (SMART) for solar flare forecasting from two datasets: the full dataset of SMART detections from 1996 to 2010 that has been previously studied by Ahmed et al. (2011) and a subset of that dataset which only includes detections that are NOAA active regions (ARs). Main contributions: we use marginal relevance as a filter feature selection method to identify most useful SMART MF properties for separating flaring from non-flaring detections and logistic regression to derive classification rules to predict future observations. For comparison, we employ a Random Forest, Support Vector Machine and a set of Deep Neural Network models, as well as Lasso for feature selection. Using the linear model with three features we obtain significantly better results (TSS=0.84) to those reported by Ahmed et al.(2011) for the full dataset of SMART detections. The same model produced competitive results (TSS=0.67) for the dataset of SMART detections that are NOAA ARs which can be compared to a broader section of flare forecasting literature. We show that more complex models are not required for this data.
K. Domijan, D. Bloomfield and F. Pitie
Fri, 7 Dec 18
48/66
Comments: Accepted for publication in Solar Physics. 22 pages, 6 figures, 8 tables
You must be logged in to post a comment.