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| 1 | 基于机器学习相似度算法的Kp指数预报显示文摘基于机器学习中的相似度算法,建立了在历史太阳风数据中寻找与当前太阳风特征相近事例的推荐模型,用来预报地磁Kp指数。使用1998-2019年间随机选择的120个太阳风事例作为测试数据集,该模型能够推荐得到历史上与输入太阳风造成相似地磁影响的太阳风事例,最优事例的Kp指数与实际值的均方根误差为0.79,相关系数为0.93。本文的推荐模型不仅能获得推荐的太阳风事例的地磁Kp指数用作预报,还可以给出太阳风特征参数按时间序列变化情况对比,让预报员可以更好地结合自身经验进行预报。 | 王子思禹 师立勤 刘四清 钟秋珍 陈艳红 闫晓辉 石育榕 何欣燃 | 2022 | 空间科学学报2022，42，2： | 1 |
| 2 | Development of New Capabilities Using Machine Learning for Space Weather Prediction显示文摘With the development of space exploration and space environment measurements,the numerous observations of solar,solar wind,and near Earth space environment have been obtained in last 20 years.The accumulation of multiple data makes it possible to better use machine learning technique,which has achieved unforeseen results in industrial applications in last decades,for developing new approaches and models in space weather investigation and prediction.In this paper,the efforts on the forecasting methods for space weather indices,events,and parameters using machine learning are briefly introduced based on the study works in recent years.These investigations indicate that machine learning,especially deep learning technique can be used in automatic characteristic identification,solar eruption prediction,space weather forecasting for solar and geomagnetic indices,and modeling of space environment parameters. | LIU Siqing CHEN Yanhong LUO Bingxian CUI Yanmei ZHONG Qiuzhen WANG Jingjing YUAN Tianjiao HU Qinghua HUANG Xin CHEN Hong | 2020 | 空间科学学报2020，40，5： | 0 |
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