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Title:

Machine Learning Model Development for Space Weather Forecasting in the Ionosphere

Document type:
Konferenzbeitrag
Contribution type:
Textbeitrag / Aufsatz
Author(s):
Natras R., Schmidt M.
Chapter contribution:
1st Workshop on Complex Data Challenges in Earth Observation (CDCEO'21)
Abstract:
In this paper, the workflow of the machine learning model development for the space weather forecast in the Earth’s ionosphere is presented, as an ongoing project. The problem of space weather forecasting using traditional approaches is discussed, as well as the advantages of using machine learning instead. In addition, the methods and approaches for building a machine learning model are presented, together with challenges related to data and algorithms. The machine learning workflow for the pro...     »
Keywords:
Machine Learning, Deep Learning, Model Development, Ionosphere, Space Weather Forecast
Editor:
Gao Cong, Maya Ramanath
Book / Congress title:
Proceedings of the CIKM 2021 Workshops co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021)
Volume:
3052
Organization:
Institute of Advanced Research in Artificial Intelligence (IARAI), Austria
Date of congress:
2021-11-01
Publisher:
Sun SITE, Informatik V, RWTH Aachen
Publisher address:
Aachen, Germany
Date of publication:
21.12.2021
Year:
2021
E-ISBN:
1613-0073
Bookseries title:
CEUR Workshop Proceedings (CEUR-WS.org)
Reviewed:
ja
WWW:
https://ceur-ws.org/Vol-3052/short10.pdf
TUM Institution:
Deutsches Geodätisches Forschungsinstitut (DGFI-TUM)
CC license:
by, http://creativecommons.org/licenses/by/4.0
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