Application of support vector machine combined with K-nearest neighbors in solar flare and solar proton events forecasting
Application of support vector machine combined with K-nearest neighbors in solar flare and solar proton events forecasting
复制标题
支持向量机结合K近邻在太阳耀斑和太阳质子事件预测中的应用
DOI：
10.1016/j.asr.2007.12.015
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发表时间：
2008-11-03
影响因子：
2.6
通讯作者：
Wang, Huaning
中科院分区：
文献类型：
--
作者：
Li, Rong;Cui, Yanmei;Wang, Huaning
将支持向量机（SVM）和K近邻（KNN）相结合，称为SVM-KNN方法，是一种综合了SVM和KNN优点的新的分类算法。将该方法应用于太阳耀斑和质子事件的预报模型中。耀斑预报模型的输入参数为太阳黑子面积、黑子磁类、黑子群McIntosh类和10 cm太阳射电通量;质子事件预报模型的输入参数为活动区经度、软X射线通量和耀斑预报模型的输入参数。对这两种预测模型进行了详细的测试，并对SVM-KNN和SVM方法进行了比较。测试结果表明，SVM-KNN方法提供了更高的预测精度相比，支持向量机。同时，它还增加了“低”预测的比率。“低”预测意味着太阳耀斑或质子事件的发生，而预测不会发生。该方法为太阳耀斑和质子事件的预报模型提供了一种新的方法。(C)空间研委会。由爱思唯尔有限公司出版。保留所有权利。
The support vector machine (SVM) combined with K-nearest neighbors (KNN), called the SVM-KNN method, is new classing algorithm that take the advantages of the SVM and KNN. This method is applied to the forecasting models for solar flares and proton events. For the solar flare forecasting model, the sunspot area, the sunspot magnetic class, and the McIntosh class of sunspot group and 10 cm solar radio flux are chosen as inputs; for the solar proton event forecasting model, the inputs include the longitude of active regions, the flux of soft X-ray, and those for the solar flare forecasting model. Detailed tests are implemented for both of the proposed forecasting models, in which the SVM-KNN and the SVM methods are compared. The testing results demonstrate that the SVM-KNN method provide a higher forecasting accuracy in contrast to the SVM. It also gives an increased rate of 'Low' prediction at the same time. The 'Low' prediction means occurrence of solar flares or proton events with predictions of non-occurrence. This method show promise for forecasting models of solar flare and proton events. (C) 2008 COSPAR. Published by Elsevier Ltd. All rights reserved.