Short-term solar flare prediction using multi-model integration method

作     者：Jin-Fu Liu Fei Li Jie Wan Da-Ren Yu 

作者机构：School of Energy Science and Engineering Harbin Institute of Technology Harbin 150001 China 

出 版 物：《Research in Astronomy and Astrophysics》 (天文和天体物理学研究（英文版）)

年 卷 期：2017年第17卷第4期

页      面：23-34页

核心收录：

学科分类：07[理学] 070401[理学-天体物理] 0704[理学-天文学] 

基　　金：supported by the National Natural Science Foundation of China(Grant No.11078010) SOHO is a project of international cooperation between the European Space Agency(ESA) and NASA 

主　　题：methods: statistical - Sun activity - Sun' magnetic fields - Sun' photosphere - Sun flares 

摘      要：A multi-model integration method is proposed to develop a multi-source and heterogeneous model for short-term solar flare prediction. Different prediction models are constructed on the basis of extracted predictors from a pool of observation databases. The outputs of the base models are normal- ized first because these established models extract predictors from many data resources using different prediction methods. Then weighted integration of the base models is used to develop a multi-model integrated model （MIM）. The weight set that single models assign is optimized by a genetic algorithm. Seven base models and data from Solar and Heliospheric Observatory/Michelson Doppler Imager lon- gitudinal magnetograms are used to construct the MIM, and then its performance is evaluated by cross validation. Experimental results showed that the MIM outperforms any individual model in nearly every data group, and the richer the diversity of the base models, the better the performance of the MIM. Thus, integrating more diversified models, such as an expert system, a statistical model and a physical model, will greatly improve the performance of the MIM.