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Solar Flare Forecasting from Magnetic Feature Properties Generated by the Solar Monitor Active Region Tracker

机译：太阳能监视器有源区跟踪器产生的磁性特征性能的太阳峰预测

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摘要

We study the predictive capabilities of magnetic-feature properties (MF) generated by the Solar Monitor Active Region Tracker (SMART: Higgins et al. in Adv. Space Res. 47, 2105, 2011) for solar-flare forecasting from two datasets: the full dataset of SMART detections from 1996 to 2010 which has been previously studied by Ahmed et al. (Solar Phys. 283, 157, 2013) and a subset of that dataset that only includes detections that are NOAA active regions (ARs). The main contributions of this work are: we use marginal relevance as a filter feature selection method to identify the 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 (True Skill Score: TSS = 0.84) than those reported by Ahmed et al. (Solar Phys. 283, 157, 2013) 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.
机译：我们研究了太阳能监视器有源区跟踪器生成的磁性特性（MF）的预测功能（SMART：HIGGINS等。在ADV。SPACE RES。47,2105,1211）用于两个数据集的太阳耀斑预测： 1996年至2010年的智能检测完整数据集已由Ahmed等人研究过。 （太阳能物理学。283,157,2013）和该数据集的子集仅包括NOAA活动区域（ARS）的检测。这项工作的主要贡献是：我们使用边缘相关性作为过滤器特征选择方法，以识别最有用的智能MF属性，用于从非辐射检测和逻辑回归分离速降，以导出分类规则来预测未来的观察。为了比较，我们采用随机森林，支持向量机和一组深神经网络模型，以及套索进行特征选择。使用具有三个特征的线性模型，我们获得明显更好的结果（真正的技能评分：TSS = 0.84），而不是Ahmed等人报告的结果。 （太阳能系统。283,157,2013）用于智能检测的完整数据集。对于NOAA ARS的智能检测数据集，相同的模型产生了竞争力（TSS = 0.67），这可以与耀斑预测文献的更广泛的部分进行比较。我们显示此数据不需要更复杂的模型。

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