@inproceedings{c784eff356e24d9e8474be7e649337fc,
title = "Short-term prediction of ionospheric total electron content based on deep neural network models",
abstract = "This study utilizes ionospheric TEC data from Englewood, Florida (26.95°N, 82.35°W) in April and September 2024 in the United States. Transformer, CNN-LSTM and LSTM neural network model are used to generate predictions and evaluate the predictive performance of the neural network models. The research results prove that CNN-LSTM neural network model proves effective in predicting various ionospheric parameters and demonstrates notable improvements in accuracy compared to traditional LSTM neural network models. The Transformer model exhibits a significant enhancement in prediction accuracy over LSTM and CNN-LSTM, albeit at the cost of a notable increase in computational time.",
keywords = "CNN-LSTM, LSTM, Transformer, ionospheric irregularities, short-term forecast",
author = "Fangzhou Wu and Di Yao and Changjun Yu",
note = "Publisher Copyright: {\textcopyright} 2026 SPIE.; 2026 International Conference on Remote Sensing, Surveying, and Mapping, RSSM 2026 ; Conference date: 16-01-2026 Through 18-01-2026",
year = "2026",
month = may,
day = "19",
doi = "10.1117/12.3112341",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Fei Meng and Hongquan Song",
booktitle = "International Conference on Remote Sensing, Surveying, and Mapping, RSSM 2026",
address = "美国",
}