- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource, 1 online resource.
- Language
-
Englisch
- Bibliographic citation
-
Machine learning techniques in estimation of eggplant crop evapotranspiration ; volume:13 ; number:6 ; day:22 ; month:5 ; year:2023 ; pages:1-30 ; date:6.2023
Applied water science ; 13, Heft 6 (22.5.2023), 1-30, 6.2023
- Classification
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Landwirtschaft, Veterinärmedizin
- Creator
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Cemek, Bilal
Tasan, Sevda
Canturk, Aslıhan
Tasan, Mehmet
Simsek, Halis
- Contributor
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SpringerLink (Online service)
- DOI
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10.1007/s13201-023-01942-1
- URN
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urn:nbn:de:101:1-2023091615435767247035
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 11:01 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Cemek, Bilal
- Tasan, Sevda
- Canturk, Aslıhan
- Tasan, Mehmet
- Simsek, Halis
- SpringerLink (Online service)
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Estimation of the crop evapotranspiration for Udham Singh Nagar district using modified Priestley-Taylor model and Landsat imagery
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Revealing accuracy in climate dynamics: enhancing evapotranspiration estimation using advanced quantile regression and machine learning models
Estimation of irrigated crop artificial irrigation evapotranspiration in China
High performance machine learning approach for reference evapotranspiration estimation
Enhancing references evapotranspiration forecasting with teleconnection indices and advanced machine learning techniques
Estimation reference crop evapotranspiration (ET0) using artificial intelligence model in an arid climate with external data
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Estimation of the crop evapotranspiration for Udham Singh Nagar district using modified Priestley-Taylor model and Landsat imagery
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