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首页> 外文期刊>Space Weather >Prediction of the level of ionospheric scintillation at equatorial latitudes in Brazil using a neural network
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Prediction of the level of ionospheric scintillation at equatorial latitudes in Brazil using a neural network

机译：使用神经网络预测巴西赤道纬度的电离层闪烁水平

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摘要

Electron density irregularity structures, often associated with ionospheric plasma bubbles, drive amplitude and phase fluctuations in radio signals that, in turn, create a phenomenon known as ionospheric scintillation. The phenomenon occurs frequently around the magnetic equator where plasma instability mechanisms generate postsunset plasma bubbles and density depletions. A previous correlation study suggested that scintillation at the magnetic equator may provide a forecast of subsequent scintillation at the equatorial ionization anomaly southern peak. In this work, it is proposed to predict the level of scintillation over São Luís (2.52°S, 44.3°W; dip latitude: ∼2.5°S) near the magnetic equator with lead time of hours but without specifying the moment at which the scintillation starts or ends. A collection of extended databases relating scintillation to ionospheric variables for São Luís is employed to perform the training of an artificial neural network with a new architecture. Two classes are considered, not strong (null/weak/moderate) and strong scintillation. An innovative scheme preprocesses the data taking into account similarities of the values of the variables for the same class. A formerly proposed resampling heuristic is employed to provide a balanced number of tuples of each class in the training set. Tests were performed showing that the proposed neural network is able to predict the level of scintillation over the station on the evening ahead of the data sample considered between 17:30 and 19:00 LT.
机译：通常与电离层等离子体气泡相关的电子密度不规则结构会驱动无线电信号的幅度和相位波动，进而产生一种称为电离层闪烁的现象。该现象经常发生在磁赤道附近，在该处，等离子体的不稳定性机制会产生未凝固的等离子体气泡和密度耗尽。先前的相关研究表明，在磁赤道发生的闪烁可能提供对随后在赤道电离异常南峰发生的闪烁的预测。在这项工作中，建议预测磁赤道附近圣路易斯的闪烁水平（2.52°S，44.3°W；倾角：〜2.5°S），前置时间为小时，但不指定闪烁开始或结束。使用了与圣路易斯电离层变量有关的闪烁相关的扩展数据库集合，以执行具有新架构的人工神经网络的训练。考虑了两类，不强（空/弱/中）和强闪烁。一种创新的方案会考虑相同类别变量值的相似性来预处理数据。使用以前提出的重采样启发式方法来提供训练集中每个班级的平衡元组。进行的测试表明，提出的神经网络能够在LT的17:30到19:00之间考虑的数据样本之前的晚上，预测车站的闪烁水平。

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  • 来源
    《Space Weather》 |2015年第8期|446-457|共12页
  • 作者

    G. R. T. de Lima; S. Stephany; E. R. de Paula; I. S. Batista; M. A. Abdu;

  • 作者单位

    National Institute for Space Research, São José dos Campos, Brazil, National Centre for Monitoring and Warnings of Natural Disasters, São José dos Campos, Brazil;

    National Institute for Space Research, São José dos Campos, Brazil;

    National Institute for Space Research, São José dos Campos, Brazil;

    National Institute for Space Research, São José dos Campos, Brazil;

    National Institute for Space Research, São José dos Campos, Brazil;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Indexes; Neural networks; Plasmas; Electron mobility; Correlation; Predictive models;

    机译：索引;神经网络;等离子;电子迁移率;相关性;预测模型;

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