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Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Natras, Randa ; Soja, Benedikt ; Schmidt, Michael
Titel:
Uncertainty Quantification for Machine Learning-Based Ionosphere and Space Weather Forecasting: Ensemble, Bayesian Neural Network, and Quantile Gradient Boosting
Stichworte:
COMPUTATIONAL GEOPHYSICS ; Neural networks, fuzzy logic, machine learning ; EXPLORATION GEOPHYSICS ; Gravity methods ; GEODESY AND GRAVITY ; Transient deformation ; Tectonic deformation ; Time variable gravity ; Gravity anomalies and Earth structure ; Satellite geodesy: results ; Seismic cycle related deformations ; HYDROLOGY ; Estimation and forecasting ; Uncertainty assessment ; INFORMATICS ; Machine learning ; Forecasting ; Uncertainty ; IONOSPHERE ; Modeling and forecastin...     »
Zeitschriftentitel:
Space Weather
Jahr:
2023
Band / Volume:
21
Heft / Issue:
10
Volltext / DOI:
doi:10.1029/2023sw003483
E-ISSN:
1542-7390
Publikationsdatum:
04.10.2023
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