基于集成学习的太阳质子事件短期预报方法  

Solar Proton Events Short-time Forecasting Based on Ensemble Learning

作　　者：宫哲 邹自明 陆阳 GONG Zhe;ZOU Ziming;LU Yang(National Space Science Center,Chinese Academy of Sciences,Beijing 100190;University of Chinese Academy of Sciences,Beijing 100049)

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

出　　处：《空间科学学报》2022年第3期340-345,共6页Chinese Journal of Space Science

基　　金：中国科学院“十三五”信息化建设专项资助(XXH13505-04)。

摘　　要：太阳质子事件是一种由太阳活动爆发时喷射并传播到近地空间的高能粒子引起的空间天气现象。这些高能粒子会对航天器和宇航员产生严重危害,对太阳质子事件进行准确的短期预报是航天活动灾害预防的重要内容。针对当前主要预报模型中普遍存在的高虚报率问题,提出了一种基于集成学习的太阳质子事件短期预报方法,利用第23个太阳活动周数据,建立了一种集成8种机器学习模型的太阳质子事件短期预报系统。实验结果表明,本文方法在取得了80.95%的报准率的同时,将虚报率降低至19.05%,相比现有的预报系统具有较为明显的优势。Solar proton event is a space weather phenomenon caused by energetic particles ejected and propagated into near-Earth space during bursts of solar activity.These high-energy particles can cause serious harm to spacecraft and astronauts,therefore,accurate short-term forecasting of solar proton events is very necessary as part of disaster prevention for space activities.The short-time forecasting of solar proton events still faces a lot of challenges,one of which is the high false alarm rate.To solve this problem,we adopted a whole new set of methods-machine learning.As a branch of computer science,many excellent algorithms have emerged in the field of machine learning in recent years,and have achieved successful applications in many fields.In this study,an ensemble model based on 8 widely used machine learning models is established to make precise forecasting of solar proton events.An experiment on the 23rd solar cycle shows that this model gets a probability of detection of 80.95% and a false alarm rate of 19.05%.

关 键 词：太阳质子事件 短期预报 集成学习 虚报率 

分 类 号：P353[天文地球—空间物理学]