Short‐Term Prediction of the Dst Index and Estimation of Efficient Uncertainty Using a Hybrid Deep Learning Network

Short‐Term Prediction of the Dst Index and Estimation of Efficient Uncertainty Using a Hybrid Deep Learning Network

Abstract In this study, we developed a novel deep learning model to predict the disturbance storm time (Dst) index 1–4 hr ahead. We also employed the Monte Carlo (MC) dropout technique to estimate the uncertainty and provide the prediction interval by introducing a recalibration factor. The proposed...

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Bibliographic Details
Main Authors: Ruyao Wang, Jianhui Wang, Tuo Liang, Huixiong Zhang
Format: Article
Language:English
Published: Wiley 2024-12-01
Series:Space Weather
Subjects:
AI
LSTM
geomagnetic storm
uncertainty estimation
deep learning
neural network
Online Access:https://doi.org/10.1029/2024SW004002
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https://doi.org/10.1029/2024SW004002

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