Prediction of SYM-H index by NARX neural network from IMF and solar wind data
Prediction of SYM-H index by NARX neural network from IMF and solar wind data
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NARX 神经网络根据 IMF 和太阳风数据预测 SYM-H 指数
DOI：
10.1007/s11431-009-0296-9
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发表时间：
2009-04
期刊：
影响因子：
--
通讯作者：
Ma ShuYing
中科院分区：
文献类型：
--
作者：
Cai HongTao;Liu RuoSi;Zhou YunLiang;Cai Lei;Ma ShuYing
SYM-H是空间天气的重要指标之一。它与Dst指数类似，但具有更高的时间分辨率。本文提出了一种基于非线性自回归的人工神经网络（NARX），用于太阳风和IMF资料预报SYM-H指数。与常用的BP网络和Elman网络相比，新的NRAX模型具有更好的预测能力。对于15个测试的大风暴，包括5个最小SYM-H <-200 nT的超级风暴，NARX网络预报的SYM-H指数与实际观测的SYM-H指数总体互相关为0.91。对于5个单独的超级风暴，与2001年3月最小SYM-H为-434 nT的超级风暴相关的系数最低为0.91;而对于最小SYM-H为-300 nT至-400 nT的两个超级风暴，相关系数分别高达0.93和0.96。模型性能的显著改善可以归因于SYM-H网络输出到输入的具有适当长度（约120 min）的关键反馈，这意味着具有适当长度历史的准实时环电流的一些信息在预测中起作用。这说明，在用人工神经网络进行SYM-H风暴时预报时，除了太阳风和IMF的直接驱动外，环电流自身的状态对SYM-H的演变尤其是恢复阶段的演变也起着重要的作用，本文建立的NARX神经网络模型为实现这一目标提供了一种有效的途径。
SYM-H is one of the important indices for space weather. It indicates the intensity of magnetic storm, similarly toDstindex but with much higher time-resolution. In this paper an artificial neural network (ANN) of Nonlinear Auto Regressive with eXogenous inputs (NARX) has been developed to predict SYM-H index from solar wind and IMF data. In comparison with usual BP and Elman network, the new NRAX model shows much better prediction capability. For 15 testing great storms including 5 super-storms of Min. SYM-H < −200 nT, the cross-correlation of SYM-H indices between NARX network predicted and really observed is 0.91 as a whole. For the 5 individual super-storms, the lowest coefficients is 0.91 relating to the super-storm of March 2001 with Min. SYM-H of −434 nT; while for the two super-storms with Min. SYM-H ranging from −300 nT to −400 nT, the correlations reach as high as 0.93 and 0.96 respectively. The remarkable improvement of the model performance can be attributed to such a key feedback from the network output of SYM-H with a suitable length (about 120 min) to the input, which implies that some information on the quasi real-time ring currents with a proper length of history does its work in the prediction. It tells us that, in addition to the direct driving by solar wind and IMF, the own status of the ring current plays an important role in its evolution especially for recovery phase and must properly be considered in storm-time SYM-H prediction by ANN. The neural network model of NARX developed in this paper provides an effective way to achieve it.
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DOI：
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发表时间：
1963-06
期刊：
--
影响因子：
--
作者：
M. Sugiura
通讯作者：
M. Sugiura
影响因子：
5.2
作者：
H. Lundstedt;H. Gleisner;P. Wintoft
通讯作者：
H. Lundstedt;H. Gleisner;P. Wintoft
影响因子：
2.8
作者：
BURTON, RK;MCPHERRON, RL;RUSSELL, CT
通讯作者：
RUSSELL, CT
影响因子：
--
作者：
Chuanbing Wang;Chuanbing Wang;J. Chao;C.‐H. Lin
通讯作者：
Chuanbing Wang;Chuanbing Wang;J. Chao;C.‐H. Lin
影响因子：
2.4
作者：
A. V. Humboldt
通讯作者：
A. V. Humboldt