Solar flare forecasting based on sequential sunspot data  

作　　者：Rong Li Jie Zhu 

机构地区：[1]School of Information Science, Beijing Wuzi University

出　　处：《Research in Astronomy and Astrophysics》2013年第9期1118-1126,共9页天文和天体物理学研究（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant Nos. 10973020 and 11273031)

摘　　要：It is widely believed that the evolution of solar active regions leads to solar flares. However, information about the evolution of solar active regions is not employed in most existing solar flare forecasting models. In the current work, a short- term solar flare forecasting model is proposed, in which sequential sunspot data, in- cluding three days of information about evolution from active regions, are taken as one of the basic predictors. The sunspot area, the Mclntosh classification, the mag- netic classification and the radio flux are extracted and converted to a numerical for- mat that is suitable for the current forecasting model. Based on these parameters, the sliding-window method is used to form the sequential data by adding three days of information about evolution. Then, multi-layer perceptron and learning vector quanti- zation are employed to predict the flare level within 48 h. Experimental results indicate that the performance of the proposed flare forecasting model works better than previ- ous models.It is widely believed that the evolution of solar active regions leads to solar flares. However, information about the evolution of solar active regions is not employed in most existing solar flare forecasting models. In the current work, a short- term solar flare forecasting model is proposed, in which sequential sunspot data, in- cluding three days of information about evolution from active regions, are taken as one of the basic predictors. The sunspot area, the Mclntosh classification, the mag- netic classification and the radio flux are extracted and converted to a numerical for- mat that is suitable for the current forecasting model. Based on these parameters, the sliding-window method is used to form the sequential data by adding three days of information about evolution. Then, multi-layer perceptron and learning vector quanti- zation are employed to predict the flare level within 48 h. Experimental results indicate that the performance of the proposed flare forecasting model works better than previ- ous models.

关 键 词：Sun： flares -- sunspots -- machine learning 

分 类 号：P182.52[天文地球—天文学]