The Comparison of Predicting Storm-Time Ionospheric TEC by Three Methods: ARIMA, LSTM, and Seq2Seq
The Comparison of Predicting Storm-Time Ionospheric TEC by Three Methods: ARIMA, LSTM, and Seq2Seq
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ARIMA、LSTM、Seq2Seq 三种方法预测风暴期电离层 TEC 的比较
DOI：
10.3390/atmos11040316
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发表时间：
2020
期刊：
影响因子：
2.9
通讯作者：
Zhiping Wu
中科院分区：
文献类型：
--
作者：
Rongxin Tang;Fantao Zeng;Zhou Chen;Jing-Song Wang;Chun-Ming Huang;Zhiping Wu
在强磁暴期间，电离层结构通常会发生剧烈变化，这将对短波通信和卫星导航系统产生重大影响。对极端空间天气条件下的电离层进行准确的预报至关重要。然而，电离层预测一直是一个挑战，纯物理方法往往不能得到令人满意的结果，因为电离层的行为有很大的变化，不同的磁暴。本文为了寻找一种有效的预测方法，一种传统的数学方法（自回归积分移动平均-ARIMA）和两种深度学习算法（长短期记忆-LSTM和序列-序列-Seq 2Seq）进行电离层TEC的短期预测（总电子含量）在不同磁暴条件下的MIT（马萨诸塞州理工学院）牧歌观察，2001年至2016年。在极端条件下，这些方法的预报性能都存在一定的局限性，当风暴强度较大时，这些方法的有效预报时间都较短。统计分析表明，LSTM可以达到最佳的预测精度，并对强磁暴的准确趋势预测具有鲁棒性。相比之下，ARIMA和Seq 2Seq对强磁暴的预测性能相对较差。这项研究为深度学习在空间天气预报中的应用带来了新的见解。
Ionospheric structure usually changes dramatically during a strong geomagnetic storm period, which will significantly affect the short-wave communication and satellite navigation systems. It is critically important to make accurate ionospheric predictions under the extreme space weather conditions. However, ionospheric prediction is always a challenge, and pure physical methods often fail to get a satisfactory result since the ionospheric behavior varies greatly with different geomagnetic storms. In this paper, in order to find an effective prediction method, one traditional mathematical method (autoregressive integrated moving average—ARIMA) and two deep learning algorithms (long short-term memory—LSTM and sequence-to-sequence—Seq2Seq) are investigated for the short-term predictions of ionospheric TEC (Total Electron Content) under different geomagnetic storm conditions based on the MIT (Massachusetts Institute of Technology) madrigal observation from 2001 to 2016. Under the extreme condition, the performance limitation of these methods can be found. When the storm is stronger, the effective prediction horizon of the methods will be shorter. The statistical analysis shows that the LSTM can achieve the best prediction accuracy and is robust for the accurate trend prediction of the strong geomagnetic storms. In contrast, ARIMA and Seq2Seq have relatively poor performance for the prediction of the strong geomagnetic storms. This study brings new insights to the deep learning applications in the space weather forecast.