留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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, 2022, 42(5): 862-872. 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, 2022, 42(5): 862-872. doi: 10.11728/cjss2022.05.210315028
HE Xinran, ZHONG Qiuzhen, CUI Yanmei, LIU Siqing, SHI Yurong, YAN Xiaohui, WANG Zisiyu. Solar Flare Short-term Forecast Model Based on Long and Short-term Memory Neural Network (in Chinese). Chinese Journal of Space Science, 2022, 42(5): 862-872 doi: 10.11728/cjss2022.05.210315028
Citation: HE Xinran, ZHONG Qiuzhen, CUI Yanmei, LIU Siqing, SHI Yurong, YAN Xiaohui, WANG Zisiyu. Solar Flare Short-term Forecast Model Based on Long and Short-term Memory Neural Network (in Chinese). Chinese Journal of Space Science, 2022, 42(5): 862-872 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

Solar Flare Short-term Forecast Model Based on Long and 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,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方法选取权重、增益率和覆盖率均较高的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 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) 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 h (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)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) 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 class flares occurrance in the next 48 h for this sample

    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} $遗忘门函数
    $ {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
  • [1] MCINTOSH P S. The classification of sunspot groups[J]. Solar Physics, 1990, 125(2): 251-267 doi: 10.1007/BF00158405
    [2] CROWN M D. Validation of the NOAA space weather prediction center’s solar flare forecasting look-up table and forecaster-issued probabilities[J]. Space Weather, 2012, 10(6): S06006
    [3] MURRAY S A, BINGHAM S, SHARPE M, et al. Flare forecasting at the met office space weather operations Centre[J]. Space Weather, 2017, 15(4): 577-588 doi: 10.1002/2016SW001579
    [4] DEVOS A, VERBEECK C, ROBBRECHT E. Verification of space weather forecasting at the regional warning center in Belgium[J]. Journal of Space Weather and Space Climate, 2014, 4: A29 doi: 10.1051/swsc/2014025
    [5] BLOOMFIELD D S, HIGGINS P A, MCATEER R T J, et al. Toward reliable benchmarking of solar flare forecasting methods[J]. The Astrophysical Journal Letters, 2012, 747(2): L41 doi: 10.1088/2041-8205/747/2/L41
    [6] SHIN S, LEE J Y, MOON Y J, et al. Development of daily maximum flare-flux forecast models for strong solar flares[J]. Solar Physics, 2016, 291(3): 897-909 doi: 10.1007/s11207-016-0869-2
    [7] ANASTASIADIS A, PAPAIOANNOU A, SANDBERG I, et al. Predicting flares and solar energetic particle events: the FORSPEF tool[J]. Solar Physics, 2017, 292(9): 134 doi: 10.1007/s11207-017-1163-7
    [8] SONG H, TAN C Y, JING J, et al. Statistical assessment of photospheric magnetic features in imminent solar flare predictions[J]. Solar Physics, 2009, 254(1): 101-125 doi: 10.1007/s11207-008-9288-3
    [9] YANG X, LIN G H, ZHANG H Q, et al. Magnetic nonpotentiality in photospheric active regions as a predictor of solar flares[J]. The Astrophysical Journal Letters, 2013, 774(2): L27 doi: 10.1088/2041-8205/774/2/L27
    [10] MURANUSHI T, SHIBAYAMA T, MURANUSHI Y H, et al. UFCORIN: a fully automated predictor of solar flares in GOES X-ray flux[J]. Space Weather, 2015, 13(11): 778-796 doi: 10.1002/2015SW001257
    [11] COLAK T, QAHWAJI R. Automated solar activity prediction: a hybrid computer platform using machine learning and solar imaging for automated prediction of solar flares[J]. Space Weather, 2009, 7(6): S06001
    [12] AHMED O W, QAHWAJI R, COLAK T, et al. Solar flare prediction using advanced feature extraction, machine learning, and feature selection[J]. Solar Physics, 2013, 283(1): 157-175 doi: 10.1007/s11207-011-9896-1
    [13] HADA-MURANUSHI Y, MURANUSHI T, ASAI A, et al. A deep-learning approach for operation of an automated realtime flare forecast[OL]. arXiv: 1606.01587, 2016
    [14] NISHIZUKA N, SUGIURA K, KUBO Y, et al. Deep flare net (DeFN) model for solar flare prediction[J]. The Astrophysical Journal, 2018, 858(2): 113 doi: 10.3847/1538-4357/aab9a7
    [15] HUANG X, WANG H N, XU L, et al. Deep learning based solar flare forecasting model. I. results for line-of-sight magnetograms[J]. The Astrophysical Journal, 2018, 856(1): 7 doi: 10.3847/1538-4357/aaae00
    [16] QAHWAJI R, COLAK T. Automatic short-term solar flare prediction using machine learning and sunspot associations[J]. Solar Physics, 2007, 241(1): 195-211 doi: 10.1007/s11207-006-0272-5
    [17] LEKA K D, BARNES G, WAGNER E. The NWRA classification infrastructure: description and extension to the discriminant analysis flare forecasting system (DAFFS)[J]. Journal of Space Weather and Space Climate, 2018, 8: A25 doi: 10.1051/swsc/2018004
    [18] DOMIJAN K, BLOOMFIELD D S, PITIÉ F. Solar flare forecasting from magnetic feature properties generated by the solar monitor active region tracker[J]. Solar Physics, 2019, 294(1): 6 doi: 10.1007/s11207-018-1392-4
    [19] AL-GHRAIBAH A, BOUCHERON L E, MCATEER R T J. An automated classification approach to ranking photospheric proxies of magnetic energy build-up[J]. Astronomy & Astrophysics, 2015, 579: A64
    [20] WANG J X, ZHOU G P, JIN C L, et al. Solar intranetwork magnetic elements: bipolar flux appearance[J]. Solar Physics, 2012, 278(2): 299-322 doi: 10.1007/s11207-012-9950-7
    [21] RUST D M, SAKURAI T, GAIZAUSKAS V, et al. Preflare state[J]. Solar Physics, 1994, 153(1/2): 1-17
    [22] HUANG X, YU D R, HU Q H, et al. Short-term solar flare prediction using predictor teams[J]. Solar Physics, 2010, 263(1/2): 175-184
    [23] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507 doi: 10.1126/science.1127647
    [24] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444 doi: 10.1038/nature14539
    [25] COLLOBERT R, WESTON J, BOTTOU L, et al. Natural language processing (almost) from scratch[J]. The Journal of Machine Learning Research, 2011, 12: 2493-2537
    [26] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90 doi: 10.1145/3065386
    [27] 李蓉, 黄鑫. 一种黑子特征自动提取的太阳耀斑模型[J]. 中国科学: 物理学 力学 天文学, 2018, 48(11): 119601

    LI Rong, HUANG Xin. Solar flare forecasting model based on automatic feature extraction of sunspots[J]. Scientia Sinica Physica, Mechanica & Astronomica, 2018, 48(11): 119601
    [28] LIU H, LIU C, WANG J T L, et al. Predicting solar flares using a long short-term memory network[J]. The Astrophysical Journal, 2019, 877(2): 121 doi: 10.3847/1538-4357/ab1b3c
    [29] CHEN T Q, GUESTRIN C. XGBoost: a scalable tree boosting system[C]//Proceedings of the 22 nd ACM SIGKDD International Conference On Knowledge Discovery And Data Mining. San Francisco: ACM, 2016: 785-794
    [30] LEE C H, LIN C R, CHEN M S. Sliding-window filtering: an efficient algorithm for incremental mining[C]//Proceedings of the Tenth International Conference on Information and Knowledge Management. Atlanta: ACM, 2001: 263-270
    [31] GOLAB L, DEHAAN D, DEMAINE E D, et al. Identifying frequent items in sliding windows over on-line packet streams[C]//Proceedings of the 3 rd ACM SIGCOMM Conference on Internet Measurement. Miami Beach: ACM, 2003: 173-178
    [32] CHANG J H, LEE W S. A sliding window method for finding recently frequent Itemsets over online data streams[J]. Journal of Information Science and Engineering, 2004, 20(4): 753-762
    [33] LIU Y J, FANG Y J, ZHU X M. Modeling of hydraulic turbine systems based on a bayesian-gaussian neural network driven by sliding window data[J]. Journal of Zhejiang University Science C, 2010, 11(1): 56 doi: 10.1631/jzus.C0910176
    [34] HINTON G E, SRIVASTAVA N, KRIZHEVSKY A, et al. Improving neural networks by preventing co-adaptation of feature detectors[OL]. arXiv: 1207.0580, 2012
    [35] SCHAPIRE R E. The strength of weak learnability[J]. Machine Learning, 1990, 5(2): 197-227
    [36] BREIMAN L, FRIEDMAN J H, OLSHEN R A, et al. Classification and regression trees[J]. Biometrics, 1984, 40(3): 874
    [37] FAWCETT T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8): 861-874 doi: 10.1016/j.patrec.2005.10.010
  • 加载中
图(9) / 表(5)
计量
  • 文章访问数:  193
  • HTML全文浏览量:  95
  • PDF下载量:  62
  • 被引次数: 0
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

目录

    /

    返回文章
    返回