Data‐Driven Forecasting of Low‐Latitude Ionospheric Total Electron Content Using the Random Forest and LSTM Machine Learning Methods
Data‐Driven Forecasting of Low‐Latitude Ionospheric Total Electron Content Using the Random Forest and LSTM Machine Learning Methods
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DOI：
10.1029/2020sw002639
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发表时间：
2021-05
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通讯作者：
G. K. Zewdie;C. Valladares;M. Cohen;David John Lary;D. Ramani;G. Tsidu
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作者：
G. K. Zewdie;C. Valladares;M. Cohen;David John Lary;D. Ramani;G. Tsidu
在这项研究中，我们使用长短期记忆（LSTM）深度循环神经网络方法提出了数据驱动的电离层总电子含量（TEC）预测。使用随机森林机器学习方法进行回归分析并估计输入参数的变量重要性。输入数据是从表征日地环境的卫星和地面测量获得的。我们估计了 34 个不同参数的相对重要性，包括太阳通量、太阳风密度和行星际磁场三个分量的速度、莱曼阿尔法、Kp、Dst 和极盖 (PC) 指数。 TEC 测量是从位于哥伦比亚波哥大（北纬 4.7110°，西经 74.0721°）的赤道 GPS 站以 15 秒的节奏进行的。 2008-2017年的数据集，包括使用随机森林估计的前五个参数，用于训练机器学习模型，2018年的数据集用于LSTM预测的独立测试。 LSTM 方法可提前 5 小时以 30 分钟的节奏预测 TEC。结果表明，在不久的将来可以做出具有低均方根 (RMS) 误差（高相关性）的非常好的预测，并且随着我们对未来的进一步预测，RMS 误差会增加。数据来源是表征日地环境的卫星和地面测量数据。
In this research, we present data‐driven forecasting of ionospheric total electron content (TEC) using the Long‐Short Term Memory (LSTM) deep recurrent neural network method. The random forest machine learning method was used to perform a regression analysis and estimate the variable importance of the input parameters. The input data are obtained from satellite and ground based measurements characterizing the solar‐terrestrial environment. We estimate the relative importance of 34 different parameters, including the solar flux, solar wind density, and speed the three components of interplanetary magnetic field, Lyman‐alpha, the Kp, Dst, and Polar Cap (PC) indices. The TEC measurements are taken with 15‐s cadence from an equatorial GPS station located at Bogota, Columbia (4.7110° N, 74.0721° W). The 2008–2017 data set, including the top five parameters estimated using the random forest, is used for training the machine learning models, and the 2018 data set is used for independent testing of the LSTM forecasting. The LSTM method as applied to forecast the TEC up to 5 h ahead, with 30‐min cadence. The results indicate that very good forecasts with low root mean square (RMS) error (high correlation) can be made in the near future and the RMS errors increase as we forecast further into the future. The data sources are satellite and ground based measurements characterizing the solar‐terrestrial environment.