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- Title
基于 SSA-LSTM 的短期电离层 TEC 组合预报模型.
- Authors
吴 晗; 黄 玲; 刘立龙; 黄良珂; 章红平
- 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.
- Publication
Journal of Geodesy & Geodynamics (1671-5942), 2022, Vol 42, Issue 6, p626
- ISSN
1671-5942
- Publication type
Article
- DOI
10.14075/j.jgg.2022.06.014