Solar Flare Prediction Using Advanced Feature Extraction, Machine Learning, and Feature Selection
Solar Flare Prediction Using Advanced Feature Extraction, Machine Learning, and Feature Selection
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DOI：
10.1007/s11207-011-9896-1
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发表时间：
2013-03-01
期刊：
影响因子：
2.8
通讯作者：
Bloomfield, D. Shaun
中科院分区：
文献类型：
--
作者：
Ahmed, Omar W.;Qahwaji, Rami;Bloomfield, D. Shaun
开发了新的机器学习和特征选择算法来研究：i)最近开发的Solar Monitor Active Region Tracker （SMART）产生的磁特征（MF）特性的耀斑预测能力；ii)与耀斑发生最显著相关的SMART的MF特性。开发了时空关联算法，将1996年4月至2010年12月期间的MFs与耀斑联系起来，以区分耀斑和非耀斑MFs，并使机器学习和特征选择算法得以应用。将机器学习算法应用于相关数据集，以确定所有21种SMART MF属性的耀斑预测能力。使用标准预测验证措施评估预测性能，并与同样基于机器学习的耀斑预测标准技术之一的预测措施进行比较：自动太阳活动预测（ASAP）。对比表明，SMART MFs与机器学习的结合有可能实现比ASAP更准确的耀斑预测。然后应用特征选择算法来确定与耀斑发生最相关的MF属性。研究发现，6个MF属性的简化集可以达到与21个SMART MF属性的完整集合相似的预测精度。
Novel machine-learning and feature-selection algorithms have been developed to study: i) the flare-prediction-capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); ii) SMART's MF properties that are most significantly related to flare occurrence. Spatiotemporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine-learning and feature-selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare-prediction-capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast-verification measures and compared with the prediction measures of one of the standard technologies for flare-prediction that is also based on machine-learning: Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine-learning has the potential to achieve more accurate flare-prediction than ASAP. Feature-selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of six MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties.