Forecasting Auroral Substorms from Observed Data with a Supervised Learning Algorithm
Forecasting Auroral Substorms from Observed Data with a Supervised Learning Algorithm
复制标题
使用监督学习算法根据观测数据预测极光亚暴
DOI：
--
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
D. Ikeda
中科院分区：
文献类型：
--
作者：
Takanori Tanaka;Daisuke Kitao;Yuka Sato;Yoshimasa Tanaka;D. Ikeda
极光是美丽的现象，吸引了很多人。然而，它的物理模型仍然是一个有争议的主题，因为它是由太阳风、磁层和电离层等不同区域相互作用造成的，而且很难在如此广泛的区域同时获得数据。本文致力于预测极光亮化的开始，然后是极光向极地扩展，称为极光亚暴。我们采用数据驱动的方法，而不是极光的物理模型。这种方法需要标记的数据，这些数据显示极光出现的时间。然而，这是具有挑战性的，因为存在来自不同地区的各种各样的观测数据，尽管它们与极光的开始时间没有联系。我们使用在挪威特罗姆索获得的全天图像来识别极光亚暴。然后，我们从许多类型的数据中选择太阳风和地磁场数据作为实现目标的第一次尝试，并将它们与已识别的极光亚暴的开始时间联系起来。利用构建的数据对典型的监督学习算法--支持向量机的分类器进行训练，在5次交叉验证下，分类器的分类正确率达到78%左右。
Auroras are beautiful phenomena and attract many people. However, its physical model still remains a subject of dispute because it is caused by the interaction of diverse areas, such as solar wind, magnetosphere, and ionosphere, and it is difficult to simultaneously obtain data in such wide areas. This paper is devoted to forecasting the onset of brightening of auroras followed by poleward expansion, called auroral substorms. We adopt a data-driven approach, instead of physical models of auroras. This approach requires labeled data, which shows when auroras appeared. However, this is challenging because there exist a wide variety of observed data from diverse areas while they are not tied with onset time of auroras. We identified auroral substorms using all-sky images obtained at Tromso, Norway. Then, we chose solar wind and geomagnetic field data as the first attempt toward the goal, out of many types of data, and associated them with the onset times of the identified auroral substorms. We trained a classifier of the support vector machine, which is a typical supervised learning algorithm, using the constructed data, and the classifier achieves around 78% classification accuracy at 5-fold cross validation.