ISNet: Decomposed Dynamic Spatio‐Temporal Neural Network for Ionospheric Scintillation Forecasts
Abstract Accurate prediction of ionospheric scintillation is essential for ensuring the reliability of spaceborne and ground‐based radio wave technology infrastructures, including but not limited to navigation and communication systems. In this study, we propose a deep learning‐based Ionospheric Sci...
Saved in:
Main Authors: | Zhixu Gao, Yanhong Chen, Xianzhi Ao, Fulu Yue, Hong Chen, Hao Deng, Bingxian Luo, Xin Wang, Tianjiao Yuan |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2025-06-01
|
Series: | Space Weather |
Online Access: | https://doi.org/10.1029/2024SW004239 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Ionospheric Time Series Prediction Method Based on Spatio-Temporal Graph Neural Network
by: Yifei Chen, et al.
Published: (2025-06-01) -
Ionospheric Scintillation and Geomagnetic Disturbance Caused by Space Hurricanes
by: Sheng Lu, et al.
Published: (2025-07-01) -
Enhancing GNSS Positioning resilience against strong ionospheric scintillation
by: J. F. G. Monico, et al.
Published: (2025-07-01) -
TEDformer: Temporal Feature Enhanced Decomposed Transformer for Long-Term Series Forecasting
by: Jiayi Fan, et al.
Published: (2025-01-01) -
Time-Series Forecasting Method Based on Hierarchical Spatio-Temporal Attention Mechanism
by: Zhiguo Xiao, et al.
Published: (2025-06-01)