Solar Proton Events Short-time Forecasting Based on Ensemble Learning
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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。Abstract: 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%.
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Key words:
- Solar proton events /
- Short-time prediction /
- Ensemble learning /
- False alarm rate
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表 2 实验数据的划分方式
Table 2. Dataset division strategy
子数据集 数量 作用 训练集 交叉训练集 400条
训练集的4/5交叉验证中训练
模型训练集成
模型交叉验证集 100条
训练集的1/5交叉验证中测试
模型能力测试集 164条 测试集成模型的能力 
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表 3 八种模型各自在交叉验证中的表现
Table 3. Performance of 8 individual models on cross-validation dataset
模型 报准率 虚报率 F1值 LR 0.5325 0.3602 0.5753 DT 0.619 0.3810 0.6190 RF 0.5714 0.1429 0.6857 Ada 0.5238 0.2143 0.6286 GBDT 0.6667 0.1765 0.7368 XGB 0.7619 0.3333 0.7111 GNB 1.0000 0.6719 0.4941 KNN 0.5714 0.3333 0.6154
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表 4 集成模型对测试集中的太阳质子事件的预报结果
Table 4. SPEs prediction result of the ensemble model on the test set
质子事件
发生时间峰值
通量是否
漏报质子事件
发生时间峰值
通量是否
漏报1998-04-20 1700 否 2002-08-14 26 是 1998-11-07 11 否 2002-08-24 317 否 1999-06-04 64 否 2003-10-26 466 否 2000-11-24 94 否 2003-11-02 30 否 2001-04-02 1110 否 2003-12-02 89 是 2001-04-12 51 否 2004-07-25 2090 否 2001-11-17 34 是 2004-11-01 63 是 2001-11-22 25 否 2005-01-20 1860 否 2002-03-15 13 否 2005-08-22 337 否 2002-04-21 2520 否 2005-09-09 1880 否 2002-07-15 234 否
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表 5 集成模型在测试集上的预报结果
Table 5. Result of the ensemble model on the test set
预报/实际 无SPE 有SPE 总计 无SPE 139 4 143 有SPE 4 17 21 总计 143 21 164
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表 6 本文模型与现行其他预报方法的对比
Table 6. Comparison with other current prediction methods
模型 报准率 虚报率 F1值 PPS 85.71% 50.00% 0.6316 Laurenza模型 63% 42% 0.6040 UMASEP 80.72% 33.99% 0.7263 Qiuzhen模型 80.00% 25.85% 0.7696 本文方法 80.95% 19.05% 0.8085
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