基于机器学习相似度算法的Kp指数预报  

Kp Index Prediction Based on Similarity Algorithm of Machine Learning

作　　者：王子思禹 师立勤 刘四清[1,2,3] 钟秋珍 陈艳红[1,3] 闫晓辉 石育榕[1,2,3] 何欣燃 WANG Zisiyu;SHI Liqin;LIU Siqing;ZHONG Qiuzhen;CHEN Yanhong;YAN Xiaohui;SHI Yurong;HE Xinran(National Space Science Center,Chinese Academy of Sciences,Beijing 100190;University of Chinese Academy of Sciences,Beijing 100049;Key Laboratory of Science and Technology on Environmental Space Situation Awareness,Chinese Academy of Sciences,Beijing 100190)

机构地区：[1]中国科学院国家空间科学中心,北京100190 [2]中国科学院大学,北京100049 [3]中国科学院空间环境态势感知技术重点实验室,北京100190

出　　处：《空间科学学报》2022年第2期199-205,共7页Chinese Journal of Space Science

基　　金：国家自然科学基金项目资助(42074224)。

摘　　要：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。The solar wind is the direct cause of the geomagnetic disturbance.In this paper,based on the feature selection and similarity algorithm of machine learning,a recommended model is established to search for cases whose characteristics are similar to the current solar wind in historical solar wind data,and to obtain the prediction of the geomagnetic Kp index.Tested on 120 solar wind cases randomly selected from 1998 to 2019,the results show that the solar wind cases which have similar geomagnetic effects to the input solar wind can be worked out successfully by proposed model.And the root mean square error between the Kp index of the optimal case recommended by the model and the actual value is 0.79,and the correlation coefficient is 0.93.Different from traditional forecast models,the proposed recommended model in this paper can not only provide a geomagnetic Kp index as a forecast,but also give a clearer and more intuitive comparison of the changes between the solar wind characteristic parameters according to the time series.Even because the historical events have already happened,we can artificially find more dimensional information of the similar historical cases,which makes forecasters better combine their own experience in Kp index forecasting.

关 键 词：太阳风 机器学习 相似度算法 地磁Kp 指数 预报 

分 类 号：P353[天文地球—空间物理学]