Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 25, issue 2
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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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.
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