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Title:

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

Document type:
Zeitschriftenaufsatz
Author(s):
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...     »
Keywords:
machine learning; ensemble learning; ionosphere; Vertical Total Electron Content (VTEC) forecasting; space weather
Journal title:
Remote Sensing
Year:
2022
Journal volume:
14
Journal issue:
15
Pages contribution:
3547
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.3390/rs14153547
Publisher:
MDPI
Impact Factor:
5.349
Status:
Verlagsversion / published
Submitted:
21.06.2022
Accepted:
16.07.2022
Date of publication:
24.07.2022
TUM Institution:
Deutsches Geodätisches Forschungsinstitut (DGFI-TUM)
CC license:
by, http://creativecommons.org/licenses/by/4.0
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