Automated flare forecasting using a statistical learning technique

Document Type

Article

Publication Date

8-1-2010

Abstract

We present a new method for automatically forecasting the occurrence of solar flares based on photospheric magnetic measurements. The method is a cascading combination of an ordinal logistic regression model and a support vector machine classifier. The predictive variables are three photospheric magnetic parameters, i.e., the total unsigned magnetic flux, length of the strong-gradient magnetic polarity inversion line, and total magnetic energy dissipation. The output is true or false for the occurrence of a certain level of flares within 24 hours. Experimental results, from a sample of 230 active regions between 1996 and 2005, show the accuracies of a 24- hour flare forecast to be 0.86, 0.72, 0.65 and 0.84 respectively for the four different levels. Comparison shows an improvement in the accuracy of X-class flare forecasting.

Identifier

77958459799 (Scopus)

Publication Title

Research in Astronomy and Astrophysics

External Full Text Location

https://doi.org/10.1088/1674-4527/10/8/008

ISSN

16744527

First Page

785

Last Page

796

Issue

8

Volume

10

This document is currently not available here.

Share

COinS