Support vector machine as an efficient tool for high‐dimensional data processing: Application to substorm forecasting

Abstract

Abstract. The support vector machine (SVM) has been used to model solar wind-driven geomagnetic substorm activity characterized by the auroral electrojet (AE) index. The focus of the present study, which is the first application of the SVM to space physics problems, is reliable prediction of large-amplitude substorm events from solar wind and interplanetary magnetic field data. This forecasting problem is important for many practical applications as well as for further understanding of the overall substorm dynamics. SVM has been trained on symbolically encoded AE index time series to perform supercfitical/subcfitical classification with respect to an application-dependent threshold. It is shown that SVM performance can be comparable to or even superior to that of the neural networks model. The advantages of the SVM-based techniques are expected to be much more pronounced in future space weather forecasting models, which will incorporate many types of high-dimensional, multiscale input data once real time availability of this information becomes technologically feasible.

Effects of solar wind ultralow‐frequency fluctuations on plasma sheet electron temperature: Regression analysis with support vector machine

“…(4) It avoids traps by local minima. In space physics, SVM has been used in forecasting, including solar F 10.7 [ Huang et al , ], solar wind velocity [ Liu et al , ], substorms [ Gavrishchaka and Ganguli , ], Kp [ Ji et al , ], and storm time ionosphere [ Sun et al , ]. SVRM has been applied to model the magnetopause shape [ Wang et al , ].…”

Application of Support Vector Machine to the Prediction of Geo-Effective Halo Cmes

“…Machine learning can automatically make a model from data, even in case that they are not clearly understood. Therefore machine learning technology has been employed for space weather applications in the following two aspects: space weather prediction (Al-Omari et al 2010;Chen et al 2010;Colak et al 2009;Gavrishchaka et al 2001;He et al 2008;Li et al 2007;Liu Corresponding Author : Y.-J. Moon et al 2011;Olmedo et al 2005;Qahwaji et al 2007Qahwaji et al , 2008Yuan et al 2011) and solar feature identification (Henwood et al 2010;Labrosse et al 2010;Quaalude et al 2003Quaalude et al , 2005Martens et al 2009).…”

Forecasting the ionospheric f0F2 parameter one hour ahead using a support vector machine technique

“…Once the kernel function is chosen, kernel representation allows one to learn effectively by choosing the underlying map and dimension N (Gavrishchaka and Ganguli, 2001).…”

“…It is found that this method provides a good compromise between the model complexity and learning ability, when there is limited input data. In particular, we note that Gavrishchaka and Ganguli (2001) used an SVM to model solar wind-driven geomagnetic substorm activity characterized by the auroral electrojet (AE) index. Qahwaji and Colak (2007) compared several machine learning algorithms for an automated short-term solar flare prediction and found that SVMs have the best performance for predicting whether a McIntosh classified sunspot group is going to flare or not.…”