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

Sattari, Mohammad Taghi; Apaydin, Halit; Band, Shahab S.; Mosavi, Amir; Prasad, Ramendra

doi:https://doi.org/10.5194/hess-25-603-2021

Articles | Volume 25, issue 2

https://doi.org/10.5194/hess-25-603-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-603-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 2

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, Halit Apaydin, Shahab S. Band, Amir Mosavi, and Ramendra Prasad

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Final revised paper (published on 10 Feb 2021)
Preprint (discussion started on 22 Jul 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

SC1: 'Referee comment', Hatice Çıtakoğlu, 11 Aug 2020
- AC1: 'Response', MohammadTaghi Sattari, 26 Sep 2020
RC1: 'reviewer opinion', Anonymous Referee #1, 17 Aug 2020
- AC2: 'Response', MohammadTaghi Sattari, 26 Sep 2020
- AC3: 'Response about completed issues', MohammadTaghi Sattari, 07 Nov 2020
- AC5: 'Response about completed issues', MohammadTaghi Sattari, 07 Nov 2020
RC2: 'Comments and Suggestions for Authors', Anonymous Referee #2, 24 Sep 2020
- AC4: 'Response about completed issues', MohammadTaghi Sattari, 07 Nov 2020
EC1: 'Editor's comment', Dimitri Solomatine, 19 Nov 2020

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

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.