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Titel:

Machine Learning Model Development for Space Weather Forecasting in the Ionosphere

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Natras R., Schmidt M.
Kapitel Beitrag:
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...     »
Stichworte:
Machine Learning, Deep Learning, Model Development, Ionosphere, Space Weather Forecast
Herausgeber:
Gao Cong, Maya Ramanath
Kongress- / Buchtitel:
Proceedings of the CIKM 2021 Workshops co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021)
Band / Teilband / Volume:
3052
Ausrichter der Konferenz:
Institute of Advanced Research in Artificial Intelligence (IARAI), Austria
Datum der Konferenz:
2021-11-01
Verlag / Institution:
Sun SITE, Informatik V, RWTH Aachen
Verlagsort:
Aachen, Germany
Publikationsdatum:
21.12.2021
Jahr:
2021
E-ISBN:
1613-0073
Serientitel:
CEUR Workshop Proceedings (CEUR-WS.org)
Reviewed:
ja
WWW:
https://ceur-ws.org/Vol-3052/short10.pdf
TUM Einrichtung:
Deutsches Geodätisches Forschungsinstitut (DGFI-TUM)
CC-Lizenz:
by, http://creativecommons.org/licenses/by/4.0
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