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.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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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)
- Creator
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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
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:54 AM CEST
Data provider
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Anilkumar, Ritu
- Bharti, Rishikesh
- Chutia, Dibyajyoti
- Aggarwal, Shiv Prasad
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Characteristics of mountain glaciers in the northern Japanese Alps
Effects of climate change on the valley glaciers of the Italian Alps
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Area and volume loss of the glaciers in the Ortles-Cevedale group (Eastern Italian Alps): controls and imbalance of the remaining glaciers
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