Articles | Volume 25, issue 2
Hydrol. Earth Syst. Sci., 25, 603–618, 2021
https://doi.org/10.5194/hess-25-603-2021
Hydrol. Earth Syst. Sci., 25, 603–618, 2021
https://doi.org/10.5194/hess-25-603-2021

Research article 10 Feb 2021

Research article | 10 Feb 2021

Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation

Mohammad Taghi Sattari et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (19 Nov 2020) by Dimitri Solomatine
AR by MohammadTaghi Sattari on behalf of the Authors (14 Dec 2020)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (15 Dec 2020) by Dimitri Solomatine
RR by Hatice Çıtakoğlu (20 Dec 2020)
ED: Publish subject to technical corrections (22 Dec 2020) by Dimitri Solomatine
AR by MohammadTaghi Sattari on behalf of the Authors (30 Dec 2020)  Author's response    Manuscript
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Short summary
The aim of study is to estimate the reference evapotranspiration (ET0) amount with artificial intelligence using minimum meteorological parameters in the Corum region, which is an agricultural center of Turkey. Kernel-based GPR and SVR and BFGS-ANN and LSTM models were used to estimate ET0 amounts in 10 different combinations. The results show that all four methods used predicted ET0 amounts at acceptable accuracy and error levels. The BFGS-ANN model showed higher success than the others.