Modeling Ionospheric TEC Using Gradient Boosting Based and Stacking Machine Learning Techniques

Modeling Ionospheric TEC Using Gradient Boosting Based and Stacking Machine Learning Techniques

Abstract Accurately predicting and modeling the ionospheric total electron content (TEC) can greatly improve the accuracy of satellite navigation and positioning and help to correct ionospheric delay. This study assesses the effectiveness of four different machine learning (ML) models in predicting...

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Bibliographic Details
Main Authors: Ayanew Nigusie, Ambelu Tebabal, Roman Galas
Format: Article
Language:English
Published: Wiley 2024-03-01
Series:Space Weather
Subjects:
total electron content
XGBoost
LightGBM
gradient boosting machine
stacking
machine learning
Online Access:https://doi.org/10.1029/2023SW003821
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https://doi.org/10.1029/2023SW003821

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