Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine-learning Methods
Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine-learning Methods
复制标题
DOI：
10.3847/1538-4365/ac9b17
复制
发表时间：
2022-09
期刊：
影响因子：
--
通讯作者：
Hewei Zhang;Qin Li;Yanxing Yang;J. Jing;J. T. Wang;Haimin Wang;Zuofeng Shang
中科院分区：
文献类型：
--
作者：
Hewei Zhang;Qin Li;Yanxing Yang;J. Jing;J. T. Wang;Haimin Wang;Zuofeng Shang
太阳耀斑，特别是M级和X级耀斑，通常与日冕物质抛射有关。它们是空间气象效应的最重要来源，可严重影响近地环境。因此，必须预测耀斑（特别是M级和X级耀斑），以减轻其破坏性和危险性后果。在这里，我们介绍了几种统计和机器学习方法来预测活动区域（AR）的耀斑指数（FI），该指数通过考虑一定时间间隔内不同类别耀斑的数量来量化AR的耀斑生产力。具体来说，我们的样本包括从2010年5月到2017年12月出现在太阳盘面上的563个AR。太阳动力学天文台上的日震和磁成像仪提供的空间气象HMI活动区补丁（SHARP）提供的25个磁参数通过代理表征储存在AR中的日冕磁能，并用作预测因子。我们研究了这些SHARP参数与AR的FI之间的关系，并使用了机器学习算法（样条回归）和resception方法（高斯噪声回归的合成少数过采样技术）。基于建立的关系，我们能够预测未来1天内给定AR的FI值。与其他四种流行的机器学习算法相比，我们的方法提高了FI预测的准确性，特别是对于大FI。此外，我们通过Borda计数方法对SHARP参数的重要性进行排序，该方法从九种不同的机器学习方法呈现的排名中计算得出。
Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections. They are the most important sources of space weather effects, which can severely impact the near-Earth environment. Thus it is essential to forecast flares (especially the M- and X-class ones) to mitigate their destructive and hazardous consequences. Here, we introduce several statistical and machine-learning approaches to the prediction of an active region’s (AR) flare index (FI) that quantifies the flare productivity of an AR by taking into account the number of different class flares within a certain time interval. Specifically, our sample includes 563 ARs that appeared on the solar disk from 2010 May to 2017 December. The 25 magnetic parameters, provided by the Space-weather HMI Active Region Patches (SHARP) from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory, characterize coronal magnetic energy stored in ARs by proxy and are used as the predictors. We investigate the relationship between these SHARP parameters and the FI of ARs with a machine-learning algorithm (spline regression) and the resampling method (Synthetic Minority Oversampling Technique for Regression with Gaussian Noise). Based on the established relationship, we are able to predict the value of FIs for a given AR within the next 1 day period. Compared with other four popular machine-learning algorithms, our methods improve the accuracy of FI prediction, especially for a large FI. In addition, we sort the importance of SHARP parameters by the Borda count method calculated from the ranks that are rendered by nine different machine-learning methods.