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基于长短期记忆神经网络的太阳耀斑短期预报

何欣燃 钟秋珍 崔延美 刘四清 石育榕 闫晓辉 王子思禹

何欣燃, 钟秋珍, 崔延美, 刘四清, 石育榕, 闫晓辉, 王子思禹. 基于长短期记忆神经网络的太阳耀斑短期预报[J]. 空间科学学报. doi: 10.11728/cjss2022.05.210315028
引用本文: 何欣燃, 钟秋珍, 崔延美, 刘四清, 石育榕, 闫晓辉, 王子思禹. 基于长短期记忆神经网络的太阳耀斑短期预报[J]. 空间科学学报. doi: 10.11728/cjss2022.05.210315028
HE Xinran, ZHONG Qiuzhen, CUI Yanmei, LIU Siqing, SHI Yurong, YAN Xiaohui, WANG Zisiyu. Research on Solar Flare Short-term Forecast Model Based on Long Short-term Memory Neural Network (in Chinese). Chinese Journal of Space Science, xxxx, x(x): x-xx doi: 10.11728/cjss2022.05.210315028
Citation: HE Xinran, ZHONG Qiuzhen, CUI Yanmei, LIU Siqing, SHI Yurong, YAN Xiaohui, WANG Zisiyu. Research on Solar Flare Short-term Forecast Model Based on Long Short-term Memory Neural Network (in Chinese). Chinese Journal of Space Science, xxxx, x(x): x-xx doi: 10.11728/cjss2022.05.210315028

基于长短期记忆神经网络的太阳耀斑短期预报

doi: 10.11728/cjss2022.05.210315028
详细信息
    作者简介:

    何欣燃:E-mail:hexinran@baidu.com

    通讯作者:

    钟秋珍 E-mail:zhongqz@nssc.ac.cn

  • 中图分类号: P354

Research on Solar Flare Short-term Forecast Model Based on Long Short-term Memory Neural Network

  • 摘要: 提出了一个基于长短期记忆神经网络的耀斑预报模型,利用过去24 h太阳活动区的磁场变化时序来构建样本,通过长短期记忆神经网络对磁场特征时序演化进行分析,从而预报未来48 h内是否发生≥M级别耀斑事件。使用的数据集为2010年5月到2017年5月所有活动区样本,选取了SDO/HMI SHARP的10个磁场特征参量。在建模过程中通过XGBoost方法选取了权重、增益率和覆盖率都较高的6个特征参量作为输入参数。通过测试对比,模型的虚报率和准确率与传统机器学习模型相近,报准率和临界成功指数分别为0.7483和0.7402,优于传统机器学习模型。模型总体效果优于传统机器学习模型。

     

  • 图  1  滑动窗口原理

    Figure  1.  Sliding window principle

    图  2  LSTM中隐含层重复模块结构

    Figure  2.  Architecture of repeat module in the hidden layer of LSTM.

    图  3  10个物理参量在所有弱学习器中的权重

    Figure  3.  Weights of t ten parameters in all weak learners

    图  5  10个物理参量在所有弱学习中的覆盖率

    Figure  5.  Cover rate of ten parameters in all weak learners

    图  4  10个物理参量在所有弱学习器中的增益率

    Figure  4.  Gain rate of ten parameters in all weak learners

    图  6  LSTM耀斑预报模型结构

    Figure  6.  LSTM flare prediction model structure

    图  7  LSTM耀斑预报模型ROC曲线,图中红点为该模型的最佳阈值所对应的TPR和FPR。

    Figure  7.  ROC curve of the LSTM flare prediction model. The red dots in the figure are the TPR and FPR corresponding to the optimal threshold of the model

    图  8  正样本(2012年3月8日09:36 UT至9日09:36 UT)各特征参数的变化(a)和GOES卫星测量(2012年3月9日00:00至12日00:00 UT)在该样本未来48 h的X射线通量变化(b),橙色垂直虚线为模型预测范围,绿色虚线为耀斑事件(2012年3月10日17:44 UT)

    Figure  8.  Positive sample (from 09:36 UT on 8 March 2012 to 09:36 UT on 9 March 2012) each characteristic parameter change (a) and the GOES satellite measurement (from 00:00 UT on 9 March 2012 to 00:00 UT on 12 March, 2012) of the X-ray flux change of the sample in the next 48 hours (b). Orange dashed line is the model prediction range, and the green dashed line is the flare event (17:44 UT on 10 March 2012)

    图  9  负样本(2011年11月16日12:48 UT至17日12:48 UT)各特征参数变化(a)与GOES卫星测量(2011年11月17日00:00 UT至20日00:00 UT)在该样本未来48 h的X射线通量变化(b),橙色虚线为模型预测范围,显示在该样本未来48 h内无≥M级耀斑发生

    Figure  9.  Negative sample (from 12:48 UT on 16 November 2011 to 12:48 UT on 17 November 2011) each characteristic parameter change (a) and the GOES satellite measurement (from 00:00 UT on 17 November 2011 to 00:00 on 20 November 2011) of the X-ray flux change of the sample in the next 48 h (b). Orange dashed line is the model prediction range, and shows that there is no ≥M in the next 48 h for this sample class flares occur

    表  1  图2中各符号含义的说明

    Table  1.   Implication of Some Symbols in Figure 2

    数学符号表示含义
    $ {x}_{t} $当前t时刻的输入
    $ {h}_{t} $t时刻的输出
    $ {h}_{t-1} $$t-1 $时刻的输出
    $ {C}_{t} $t时刻的细胞状态
    $ {C}_{t-1} $$t-1 $时刻的单元状态
    $ {\tilde {C}}_{t} $t时刻的候选状态值
    $ {f}_{t} $遗忘们函数
    $ {i}_{t} $输入门函数
    $ {o}_{t} $输出门函数
    $ \sigma ,\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{h} $激活函数
    下载: 导出CSV

    表  2  不同模型预报结果评估(1)

    Table  2.   Evaluation of forecast results of different models (1)

    预报方法TPR
    (报准率)
    FPR
    (虚报率)
    Accuracy
    (准确率)
    TSS
    (临界成功指数)
    LSTM0.74830.00810.98940.7402
    XGBoost0.50330.01430.98960.4890
    SVM0.33110.00820.98990.3229
    RandomForest0.32450.01730.98910.3072
    Logistic Regression0.19870.00240.99060.1963
    C4.50.48340.03870.98060.4447
    下载: 导出CSV

    表  3  不同模型预报结果评估(2)

    Table  3.   Evaluation of forecast results of different models (2 )

    预报方法真实为正且预测为正(TP)真实为正预测为负(FN)真实为负预测为负(TN)真实为负预测为正(FP)
    LSTM1133813840113
    XGBoost76741388073
    SVM491021391241
    RandomForest481031389657
    Logistic Regression301211394112
    C4.5668513743210
    下载: 导出CSV

    表  4  CNN与LSTM模型预报结果评估(1)

    Table  4.   Evaluation of forecast results of CNN and LSTM models (1)

    预报方法TPR(报准率)FPR(虚报率)Accuracy(准确率)TSS(临界成功指数)
    LSTM0.74830.00810.98940.7402
    Ref. [27] CNN0.80500.18600.79190.6190
    Ref. [28] CNN0.81000.19000.81110.6200
    下载: 导出CSV

    表  5  LSTM与CNN模型预报结果评估(2)

    Table  5.   Evaluation of forecast results of LSTM and CNN models (2)

    预报方法真实为正且预测为正(TP)真实为正预测为负(FN)真实为负预测为负(TN)真实为负预测为正(FP)
    LSTM1133813840113
    Ref. [27] CNN1008291978231
    Ref. [28] CNN1614378377118783
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-03-12
  • 录用日期:  2021-05-19
  • 修回日期:  2021-05-17
  • 网络出版日期:  2022-09-22

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