Long-term Ionospheric TEC Prediction Model Based on LSTM-SpatioTemporal Transformer
-
摘要: 电离层总电子含量(total electron content, TEC)是影响无线电波传播和航天活动的重要参数,但传统统计模型在应对TEC数据的高噪声、非平稳性及其复杂动态特征时表现出显著局限性。为此,本研究提出了一种结合时空Transformer(SpatioTemporalTransformer,STT)和长短期记忆网络(long short-term memory, LSTM)的组合预测模型,并引入具有注意力权重的辅助预测因子。在中国及周边地区电离层TEC预测实验中,使用2000年至2022年的数据进行训练,并将太阳峰年2023年的数据则作为测试集,重点评估不同参数组合对不同状态下电离层TEC预测性能的影响。消融实验证明本文所提组合模型的预测性能优于单一模型,带有注意力权重的辅助预测因子的组合模型在2023年测试集上的平均相对精度P均值从单一模型的87.45%提升至87.63%,而地磁平静期间和地磁暴期间的最高平均相对精度则分别达到94.34%和93.17%。此外,在测试集上持续时间最长的地磁平静期(DOY 221~244)和地磁暴期(DOY 166~181),平均相对精度P均值分别为90.98%和90.16%。这一结果表明,模型在不同电离层状态下均能保持较高的TEC预测精度。
中图分类号 P352-
关键词:
- 电离层TEC /
- 预测模型 /
- Transformer /
- LSTM
Abstract: Total electron content (TEC) in the ionosphere is a crucial parameter affecting radio wave propagation and space activities. However, traditional statistical models exhibit significant limitations in handling the high noise, non-stationarity, and complex dynamic characteristics of TEC data. To address this issue, this study proposes a hybrid prediction model combining a SpatioTemporal Transformer (STT) and a long short-term memory (LSTM) network, with the incorporation of attention-weighted auxiliary predictors. In an experimental setting for ionospheric TEC prediction over China and its surrounding regions, data from 2000 to 2022 were used for training, while data from the solar maximum year 2023 served as the test set. The study focused on evaluating the impact of different parameter combinations on TEC prediction performance under varying ionospheric conditions. Ablation experiments demonstrate that the proposed hybrid model outperforms single models. The hybrid model with attention-weighted auxiliary predictors achieved an average relative accuracy (P) of 87.63% on the 2023 test set, compared to 87.45% for the single model. The highest average relative accuracy during geomagnetically quiet and storm periods reached 94.34% and 93.17%, respectively. Furthermore, during the longest geomagnetically quiet period (DOY 221–244) and storm period (DOY 166–181) in the test set, the average relative accuracy (P) reached 90.98% and 90.16%, respectively. These results indicate that the model maintains high TEC prediction accuracy under different ionospheric conditions.-
Key words:
- Ionospheric TEC /
- Prediction Model /
- Transformer /
- LSTM
-
-
计量
- 文章访问数: 176
- HTML全文浏览量: 26
- PDF下载量: 30
-
被引次数:
0(来源:Crossref)
0(来源:其他)