| 基于SSA-LSTM的短期电离层TEC组合预报模型 |
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| 引用本文： | 吴晗,黄玲,刘立龙,黄良珂,章红平. 基于SSA-LSTM的短期电离层TEC组合预报模型[J]. 大地测量与地球动力学, 2022, 42(6): 626-630. DOI: 10.14075/j.jgg.2022.06.014 |
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| 作者姓名： | 吴晗 黄玲 刘立龙 黄良珂 章红平 |
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| 作者单位： | 桂林理工大学测绘地理信息学院,桂林市雁山街 319 号,541006;广西空间信息与测绘重点实验室,桂林市雁山街 319 号,541006,武汉大学卫星导航定位技术研究中心,武汉市珞喻路 129 号,430079 |
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| 基金项目： | 广西科技基地和人才专项;广西自然科学基金;广西空间信息与测绘重点实验室基金 |
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| 摘 要： | 针对电离层总电子含量(TEC)时间序列具有高噪声、非线性和非平稳的特性，在奇异谱分析基础上，融合长短期记忆神经网络模型构建短期电离层组合预报改进模型，并对磁暴期、磁平静期的电离层TEC预报精度进行分析。结果表明，在磁暴期和磁平静期，该模型预报3 d的TEC相对精度分别为91.17%和95.46%，比单一LSTM模型分别提高4.92百分点和3.17百分点。
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| 关 键 词： | 长短期记忆神经网络 奇异谱分析 地磁活动 电离层TEC预报 |
Short Term Prediction Model of Ionospheric TEC Based on SSA-LSTM |
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| WU Han,HUANG Ling,LIU Lilong,HUANG Liangke,ZHANG Hongping. Short Term Prediction Model of Ionospheric TEC Based on SSA-LSTM[J]. Journal of Geodesy and Geodynamics, 2022, 42(6): 626-630. DOI: 10.14075/j.jgg.2022.06.014 |
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| Authors: | WU Han HUANG Ling LIU Lilong HUANG Liangke ZHANG Hongping |
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| Abstract: | Aiming at the high noise, nonlinear and non-stationary dynamic characteristics of ionospheric total electron content(TEC) time series, we construct an improved short-term ionospheric combined prediction model based on singular spectrum analysis(SSA) and long short term memory(LSTM) neural network model, to realize the model’s ionospheric TEC prediction during magnetic storms and magnetic quiet periods and analyze its accuracy. The results show that the relative accuracy of model is 91.17% and 95.46% respectively, which is 4.92 percent and 3.17 percent higher than that of single LSTM model, during the period of magnetic explosion and magnetic calm. |
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| Keywords: | long-short term memory neural network singular spectrum analysis(SSA) geomagnetic activity ionospheric TEC prediction |
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