Predicting CME arrival time through data integration and ensemble learning
Predicting CME arrival time through data integration and ensemble learning
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DOI：
10.3389/fspas.2022.1013345
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发表时间：
2022-10
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通讯作者：
Khalid A. Alobaid;Yasser Abduallah;J. T. Wang;Haimin Wang;Haodi Jiang;Yan Xu;V. Yurchyshyn;Hongyang Zhang;H. Cavus;J. Jing
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作者：
Khalid A. Alobaid;Yasser Abduallah;J. T. Wang;Haimin Wang;Haodi Jiang;Yan Xu;V. Yurchyshyn;Hongyang Zhang;H. Cavus;J. Jing
太阳不断地向日光层释放辐射和等离子体。太阳偶尔会爆发耀斑和日冕物质抛射(CME)等太阳活动。日冕物质抛射带走了大量的质量和磁通量。地球指向的CME可能会对人类系统造成严重后果。它可以摧毁电网/管道、卫星和通讯。因此，准确监测和预测日冕物质抛射对于最大限度地减少对人类系统的损害具有重要意义。在这项研究中，我们提出了一种集合学习方法，称为CMETNet，用于预测CMES从太阳到地球的到达时间。我们收集和整合了1996年至2021年两个太阳周期#23和#24的喷发事件，总共有363次地球有效日冕物质抛射。用于进行预报的数据包括CME特征、太阳风参数和从SOHO/LASCOC2日冕仪获得的CME图像。我们的集成学习框架包括用于数值数据分析的回归算法和用于图像处理的卷积神经网络。实验结果表明，CMETNet的性能优于现有的机器学习方法，皮尔逊积矩相关系数为0.83，平均绝对误差为9.75h。
The Sun constantly releases radiation and plasma into the heliosphere. Sporadically, the Sun launches solar eruptions such as flares and coronal mass ejections (CMEs). CMEs carry away a huge amount of mass and magnetic flux with them. An Earth-directed CME can cause serious consequences to the human system. It can destroy power grids/pipelines, satellites, and communications. Therefore, accurately monitoring and predicting CMEs is important to minimize damages to the human system. In this study we propose an ensemble learning approach, named CMETNet, for predicting the arrival time of CMEs from the Sun to the Earth. We collect and integrate eruptive events from two solar cycles, #23 and #24, from 1996 to 2021 with a total of 363 geoeffective CMEs. The data used for making predictions include CME features, solar wind parameters and CME images obtained from the SOHO/LASCO C2 coronagraph. Our ensemble learning framework comprises regression algorithms for numerical data analysis and a convolutional neural network for image processing. Experimental results show that CMETNet performs better than existing machine learning methods reported in the literature, with a Pearson product-moment correlation coefficient of 0.83 and a mean absolute error of 9.75 h.