Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale
Abstract 3 cm- 3 3 cm- 3 3 cm- 3 3 cm- 3 r 3 cm- 3 r scores of above 0.6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and Extreme Gradient Boosting (XB) performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability with respect to predicting SSM compared with the optimised or base ML algorithms; this indicates their huge potential applicability in estimating water cycle budgets, managing irrigation, and predicting crop yields.
- 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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Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale ; volume:16 ; number:20 ; year:2023 ; pages:5825-5845 ; extent:21
Geoscientific model development ; 16, Heft 20 (2023), 5825-5845 (gesamt 21)
- Urheber
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Han, Qianqian
Zeng, Yijian
Zhang, Lijie
Cira, Calimanut-Ionut
Prikaziuk, Egor
Duan, Ting
Wang, Chao
Szabó, Brigitta
Manfreda, Salvatore
Zhuang, Ruodan
Su, Bob
- DOI
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10.5194/gmd-16-5825-2023
- URN
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urn:nbn:de:101:1-2023102604342123853506
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:57 MESZ
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Beteiligte
- Han, Qianqian
- Zeng, Yijian
- Zhang, Lijie
- Cira, Calimanut-Ionut
- Prikaziuk, Egor
- Duan, Ting
- Wang, Chao
- Szabó, Brigitta
- Manfreda, Salvatore
- Zhuang, Ruodan
- Su, Bob
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