A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA
A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA
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DOI：
10.3847/1538-4357/aaae69
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发表时间：
2018-02
期刊：
影响因子：
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通讯作者：
Jiajia Liu;Y. Ye;Chenlong Shen;Yuming Wang;R. Erd'elyi
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文献类型：
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作者：
Jiajia Liu;Y. Ye;Chenlong Shen;Yuming Wang;R. Erd'elyi
日冕物质抛射（CME）可以说是太阳系中最猛烈的喷发。日冕物质抛射会在行星际空间造成严重扰动，甚至会在许多方面影响人类活动，对基础设施造成破坏，并造成收入损失。快速准确地预测CME到达时间对于最大限度地减少CME与地球空间相互作用时可能造成的破坏至关重要。在本文中，我们提出了一种新的方法，部分/完整的晕CME到达时间预测使用机器学习算法（CAT-CAXA）。通过对CME特征和太阳风参数的详细分析，我们建立了一个预测引擎，利用182个先前观测到的地球有效的部分/完全晕CME，并使用支持向量机算法。我们证明了CAT-12 A是准确和快速的。特别是，在将CAT-CASA应用于发动机未知的测试集之后进行的预测显示，在CME到达时间内，平均绝对预测误差为1.59hr，其中54%的预测的绝对误差小于5.9hr。与其他模型的比较显示，CAT-CASA对所研究的77%的事件具有更准确的预测，可以非常快速地执行，即，在提供CME的必要输入参数的几分钟内。附录中提供了一个包含CAT-12 A引擎的实用指南和两个示例的源代码，允许社区使用CAT-12 A执行自己的预测应用程序。
Coronal mass ejections (CMEs) are arguably the most violent eruptions in the solar system. CMEs can cause severe disturbances in interplanetary space and can even affect human activities in many aspects, causing damage to infrastructure and loss of revenue. Fast and accurate prediction of CME arrival time is vital to minimize the disruption that CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar-wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full halo CMEs and using algorithms of the Support Vector Machine. We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions made after applying CAT-PUMA to a test set unknown to the engine show a mean absolute prediction error of ∼5.9 hr within the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hr. Comparisons with other models reveal that CAT-PUMA has a more accurate prediction for 77% of the events investigated that can be carried out very quickly, i.e., within minutes of providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA.