TY - GEN
T1 - Global Ionospheric Total Electron Content Forecasting Model Using Temporal Convolutional Network
AU - Xue, Kaiyu
AU - Shi, Chuang
AU - Wang, Cheng
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The ionosphere is a crucial component of Earth's atmosphere, with significant impacts on radio transmission, satellite navigation, radar surveillance, and other applications. Total Electron Content (TEC) in the ionosphere is one of the vital parameters used to describe ionospheric activity and characteristics. Due to limited observational methods, we often need the TEC predictions based on historical TEC data. In this study, we employ the Temporal Convolutional Network (TCN) architecture to construct a global ionospheric TEC prediction model. We compare the TEC predictions from the TCN model under different ionospheric activity conditions with those from time-series models and deep learning recurrent structure models. The research findings indicate that the TCN-based model offers higher prediction accuracy and greater stability for global ionospheric TEC forecasts.
AB - The ionosphere is a crucial component of Earth's atmosphere, with significant impacts on radio transmission, satellite navigation, radar surveillance, and other applications. Total Electron Content (TEC) in the ionosphere is one of the vital parameters used to describe ionospheric activity and characteristics. Due to limited observational methods, we often need the TEC predictions based on historical TEC data. In this study, we employ the Temporal Convolutional Network (TCN) architecture to construct a global ionospheric TEC prediction model. We compare the TEC predictions from the TCN model under different ionospheric activity conditions with those from time-series models and deep learning recurrent structure models. The research findings indicate that the TCN-based model offers higher prediction accuracy and greater stability for global ionospheric TEC forecasts.
KW - Temporal Convolutional Network
KW - Total Electron Content
KW - ionosphere
KW - predictions
UR - https://www.scopus.com/pages/publications/85186494725
U2 - 10.1109/CSRSWTC60855.2023.10426823
DO - 10.1109/CSRSWTC60855.2023.10426823
M3 - 会议稿件
AN - SCOPUS:85186494725
T3 - Proceedings - 2023 Cross Strait Radio Science and Wireless Technology Conference, CSRSWTC 2023
BT - Proceedings - 2023 Cross Strait Radio Science and Wireless Technology Conference, CSRSWTC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 Cross Strait Radio Science and Wireless Technology Conference, CSRSWTC 2023
Y2 - 10 November 2023 through 13 November 2023
ER -