Uncertainty Quantification for Machine Learning‐Based Ionosphere and Space Weather Forecasting: Ensemble, Bayesian Neural Network, and Quantile Gradient Boosting

Uncertainty Quantification for Machine Learning‐Based Ionosphere and Space Weather Forecasting: Ensemble, Bayesian Neural Network, and Quantile Gradient Boosting

Abstract Machine learning (ML) has been increasingly applied to space weather and ionosphere problems in recent years, with the goal of improving modeling and forecasting capabilities through a data‐driven modeling approach of nonlinear relationships. However, little work has been done to quantify t...

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Bibliographic Details
Main Authors: Randa Natras, Benedikt Soja, Michael Schmidt
Format: Article
Language:English
Published: Wiley 2023-10-01
Series:Space Weather
Subjects:
machine learning
uncertainty quantification
confidence intervals
probabilistic ionosphere forecast
space weather
ensemble
Online Access:https://doi.org/10.1029/2023SW003483
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https://doi.org/10.1029/2023SW003483

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