Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms
Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms
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DOI：
10.3847/1538-4357/835/2/156
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发表时间：
2017-02-01
影响因子：
4.9
通讯作者：
Ishii, M.
中科院分区：
文献类型：
--
作者：
Nishizuka, N.;Sugiura, K.;Ishii, M.
我们使用机器学习开发了一个耀斑预测模型，该模型经过优化以预测接下来24小时内发生的最大耀斑类别。机器学习用于设计可以从大量数据中学习并做出决策的算法。我们使用了2010-2015年期间的太阳观测数据，例如太阳动力学观测台和地球同步业务环境卫星拍摄的矢量磁图，紫外线（UV）发射和软X射线发射。我们检测活动区（AR）从全磁盘磁像，从其中提取了类似的60个功能，其时间差，包括磁中性线，电流螺旋度，紫外增亮，和耀斑的历史。在对特征数据库进行标准化之后，我们完全打乱并随机将其分为两部分进行训练和测试。为了研究哪种算法最适合耀斑预测，我们比较了三种机器学习算法：支持向量机，k-最近邻（k-NN）和极端随机树。预测得分，真实的技能统计，在完全洗牌的数据集上高于0.9，高于人类预测。结果发现，k-NN具有最高的性能在三种算法。的功能的重要性的排名表明，以前的耀斑活动是最有效的，其次是磁中性线的长度，无符号的磁通量，紫外线增亮的面积，以及超过24小时的功能的时间差，所有这些都是强相关的磁通量出现动态的AR。
We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010-2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite. We detected active regions (ARs) from the full-disk magnetogram, from which similar to 60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.