User: Guest  Login
Title:

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

Document type:
Zeitschriftenaufsatz
Author(s):
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...     »
Journal title:
Space Weather
Year:
2023
Year / month:
2023-10
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.1029/2023SW003483
WWW:
https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2023SW003483
Publisher:
American Geophysical Union (AGU)
Status:
Verlagsversion / published
Submitted:
09.03.2023
Accepted:
12.09.2023
Date of publication:
04.10.2023
TUM Institution:
Deutsches Geodätisches Forschungsinstitut (DGFI-TUM)
CC license:
by, http://creativecommons.org/licenses/by/4.0
 BibTeX