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Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Natras R., Soja B., Schmidt M.
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 the uncertainty of the results, lacking an indication of how confident and reliable the results of an ML system are. In this paper, we implement and analyze several uncertainty quantification approaches for an...     »
Zeitschriftentitel:
Space Weather
Jahr:
2023
Jahr / Monat:
2023-10
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1029/2023SW003483
WWW:
https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2023SW003483
Verlag / Institution:
American Geophysical Union (AGU)
Status:
Verlagsversion / published
Eingereicht (bei Zeitschrift):
09.03.2023
Angenommen (von Zeitschrift):
12.09.2023
Publikationsdatum:
04.10.2023
TUM Einrichtung:
Deutsches Geodätisches Forschungsinstitut (DGFI-TUM)
CC-Lizenz:
by, http://creativecommons.org/licenses/by/4.0
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