Automated flare forecasting using a statistical learning technique

作者：Yuan Yuan^*; Shih Frank Y; Jing Ju; Wang Hai Min

来源：Research in Astronomy and Astrophysics, 2010, 10(8): 785-796.

DOI：10.1088/1674-4527/10/8/008

• Sun: flares
• Sun: magnetic fields

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.

• 出版日期2010-8