Solar flare prediction using highly stressed longitudinal magnetic field parameters  

作　　者：Xin Huang Hua-Ning Wang 

机构地区：[1]Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences

出　　处：《Research in Astronomy and Astrophysics》2013年第3期351-358,共8页天文和天体物理学研究（英文版）

基　　金：supported by the National Basic Research Program of China (973 Program, Grant No. 2011CB811406);the National Natural Science Foundation of China(Grant Nos.11273031,10733020,10921303 and 11078010);the China Meteorological Administration grant (No. GYHY201106011)

摘　　要：Three new longitudinal magnetic field parameters are extracted from SOHO/MDI magnetograms to characterize properties of the stressed magnetic field in active regions, and their flare productivities are calculated for 1055 active regions. We find that the proposed parameters can be used to distinguish flaring samples from non-flaring samples. Using the long-term accumulated MDI data, we build the solar flare prediction model by using a data mining method. Furthermore, the decision boundary, which is used to divide flaring from non-flaring samples, is determined by the decision tree algorithm. Finally, the performance of the prediction model is evaluated by 10-fold cross validation technology. We conclude that an efficient solar flare prediction model can be built by the proposed longitudinal magnetic field parameters with the data mining method.Three new longitudinal magnetic field parameters are extracted from SOHO/MDI magnetograms to characterize properties of the stressed magnetic field in active regions, and their flare productivities are calculated for 1055 active regions. We find that the proposed parameters can be used to distinguish flaring samples from non-flaring samples. Using the long-term accumulated MDI data, we build the solar flare prediction model by using a data mining method. Furthermore, the decision boundary, which is used to divide flaring from non-flaring samples, is determined by the decision tree algorithm. Finally, the performance of the prediction model is evaluated by 10-fold cross validation technology. We conclude that an efficient solar flare prediction model can be built by the proposed longitudinal magnetic field parameters with the data mining method.

关 键 词：Sun： magnetic fields -- Sun： flares -- methods： statistical 

分 类 号：P182.52[天文地球—天文学]