在高太阳活动期间基于CNN-GRU神经网络模型的GNSS-VTEC预测
T Y Yang1,2, J Y Lu3,4, Y Y Yang1,2
1State Key Laboratory of Environment Characteristics and Effects for Near-space (Nanjing University of Information Science and Technology), Nanjing, 210044, China.
Scientific reports
|March 18, 2025
概括
这项研究引入了CNN-GRU模型,用于预测高太阳活动期间的总电子含量 (TEC). 该模型与传统和人工智能方法相比显示出更高的准确性,改善了空间天气预报.
科学领域:
- * 离子体物理学和太空天气.
- * 卫星导航和定位系统.
- * 电磁波的传播.
背景情况:
- * 总电子含量 (TEC) 是影响卫星导航和空间天气的关键电离层参数.
- *以前的TEC预测模型主要关注太阳活动较低的时期.
- *准确的TEC预测对于可靠的GNSS运行至关重要.
研究的目的:
- * 开发和评估一种新的CNN-GRU模型,用于在太阳活动高的情况下进行TEC预测.
- * 评估模型的性能与已建立的经验和基于人工智能的方法相比.
- * 提高太空天气预报的准确性和可靠性.
主要方法:
- *集成卷积神经网络 (CNN) 用于特征提取和门式循环单元 (GRU) 用于时间序列预测.
- *利用全球导航卫星系统 (GNSS) 来自中国三亚的单个接收器的数据.
- *对IRI,NeQuick,GRU和SVM模型进行比较分析.
主要成果:
- *CNN-GRU模型在1小时前的预测中实现了4.28 TECU的根平均平方误差 (RMSE).
- *对于24小时的预测,CNN-GRU模型显示RMSE平均值明显低,为6.94 TECU.
- * 该模型的性能优于IRI,NeQuick2,SVM和GRU,在各种条件 (包括地磁风暴) 中显示出一致的准确性.
结论:
- *CNN-GRU模型在TEC预测准确度方面取得了显著的进步,特别是在太阳活动高的时候.
- *这种方法为太空天气预报和减轻GNSS错误提供了更可靠的工具.
- * 该模型的强大性能突出显示了混合深度学习架构在地球物理学中的潜力.
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