Short-term solar flare prediction using multi-model integration method
Short-term solar flare prediction using multi-model integration method
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DOI：
10.1088/1674-4527/17/4/34
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发表时间：
2017-03
影响因子：
1.8
通讯作者：
Jinfu Liu;Fei Li;J. Wan;Daren Yu
中科院分区：
文献类型：
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作者：
Jinfu Liu;Fei Li;J. Wan;Daren Yu
提出了一种多模式集成的方法，建立了一个多源、非均匀的太阳耀斑短期预报模式。不同的预测模型构建的基础上，从一个池的观测数据库中提取的预测。基本模型的输出首先被标准化，因为这些已建立的模型使用不同的预测方法从许多数据资源中提取预测因子。然后对基础模型进行加权集成，建立多模型集成模型（MIM）。单模型分配的权重集由遗传算法优化。利用7个基本模型和太阳和日球观测站/迈克尔逊多普勒成像仪的纵向磁图数据构造了MIM，并通过交叉验证对其性能进行了评价。实验结果表明，MIM在几乎每个数据组中的性能都优于任何单个模型，并且基础模型的多样性越丰富，MIM的性能越好。因此，集成更多样化的模型，如专家系统，统计模型和物理模型，将大大提高MIM的性能。
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 normalized 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 longitudinal 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.