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Titel:

Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Natras R., Soja B., Schmidt M.
Abstract:
Space weather describes varying conditions between the Sun and Earth that can degrade Global Navigation Satellite Systems (GNSS) operations. Thus, these effects should be precisely and timely corrected for accurate and reliable GNSS applications. That can be modeled with the Vertical Total Electron Content (VTEC) in the Earth’s ionosphere. This study investigates different learning algorithms to approximate nonlinear space weather processes and forecast VTEC for 1 h and 24 h in the future for lo...     »
Stichworte:
machine learning; ensemble learning; ionosphere; Vertical Total Electron Content (VTEC) forecasting; space weather
Zeitschriftentitel:
Remote Sensing
Jahr:
2022
Band / Volume:
14
Heft / Issue:
15
Seitenangaben Beitrag:
3547
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.3390/rs14153547
Verlag / Institution:
MDPI
Impact Factor:
5.349
Status:
Verlagsversion / published
Eingereicht (bei Zeitschrift):
21.06.2022
Angenommen (von Zeitschrift):
16.07.2022
Publikationsdatum:
24.07.2022
TUM Einrichtung:
Deutsches Geodätisches Forschungsinstitut (DGFI-TUM)
CC-Lizenz:
by, http://creativecommons.org/licenses/by/4.0
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