Evaluation of water flux predictive models developed using eddy-covariance observations and machine learning: a meta-analysis
Abstract R = R = R = R = R = R n/R s T a T a R n/R s W s R n/R s is also effective. Random cross-validation showed higher model accuracy than spatial cross-validation and temporal cross-validation, but spatial cross-validation is more important in spatial extrapolation. The findings of this study are promising to guide future research on such machine-learning-based modeling.
- 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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Evaluation of water flux predictive models developed using eddy-covariance observations and machine learning: a meta-analysis ; volume:26 ; number:18 ; year:2022 ; pages:4603-4618 ; extent:16
Hydrology and earth system sciences ; 26, Heft 18 (2022), 4603-4618 (gesamt 16)
- Urheber
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Shi, Haiyang
Luo, Geping
Hellwich, Olaf
Xie, Mingjuan
Zhang, Chen
Zhang, Yu
Wang, Yuangang
Yuan, Xiuliang
Ma, Xiaofei
Zhang, Wenqiang
Kurban, Alishir
Maeyer, Philippe de
Van de Voorde, Tim
- DOI
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10.5194/hess-26-4603-2022
- URN
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urn:nbn:de:101:1-2022092205223552603965
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:37 MESZ
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Beteiligte
- Shi, Haiyang
- Luo, Geping
- Hellwich, Olaf
- Xie, Mingjuan
- Zhang, Chen
- Zhang, Yu
- Wang, Yuangang
- Yuan, Xiuliang
- Ma, Xiaofei
- Zhang, Wenqiang
- Kurban, Alishir
- Maeyer, Philippe de
- Van de Voorde, Tim
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