Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques
Abstract 0.713 1.071 m w.e. The feature importance values associated with all machine learning models suggested a high importance of meteorological variables associated with ablation. This is in line with predominantly negative mass balance observations. We conclude that machine learning techniques are promising in estimating glacier mass balance and can incorporate information from more significant meteorological variables as opposed to a simplified set of variables used in temperature index models.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques ; volume:17 ; number:7 ; year:2023 ; pages:2811-2828 ; extent:18
The Cryosphere ; 17, Heft 7 (2023), 2811-2828 (gesamt 18)
- Urheber
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Anilkumar, Ritu
Bharti, Rishikesh
Chutia, Dibyajyoti
Aggarwal, Shiv Prasad
- DOI
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10.5194/tc-17-2811-2023
- URN
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urn:nbn:de:101:1-2023072004390392956812
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:54 MESZ
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Beteiligte
- Anilkumar, Ritu
- Bharti, Rishikesh
- Chutia, Dibyajyoti
- Aggarwal, Shiv Prasad
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