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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/hess-2020-224
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-224
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  22 Jul 2020

22 Jul 2020

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This preprint is currently under review for the journal HESS.

Comparative analysis of Kernel-based versus BFGS-ANN and deep learning methods in monthly reference evaporation estimation

Mohammad Taghi Sattari1,2, Halit Apaydin3, Shahab Shamshirband4,5, and Amir Mosavi6,7 Mohammad Taghi Sattari et al.
  • 1Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 51666, Iran
  • 2Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam
  • 3Department of Agricultural Engineering, Faculty of Agriculture, Ankara University, Ankara 06110, Turkiye
  • 4Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
  • 5Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
  • 6Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany
  • 7Department of Informatics, J. Selye University, 94501 Komarno, Slovakia

Abstract. Proper estimation of the reference evapotranspiration (ET0) amount is an indispensable matter for agricultural water management in the efficient use of water. The aim of study is to estimate the amount of ET0 with a different machine and deep learning methods by using minimum meteorological parameters in the Corum region which is an arid and semi-arid climate with an important agricultural center of Turkey. In this context, meteorological variables of average, maximum and minimum temperature, sunshine duration, wind speed, average, maximum, and minimum relative humidity are used as input data monthly. Two different kernel-based (Gaussian Process Regression (GPR) and Support Vector Regression (SVR)) methods, BFGS-ANN and Long short-term memory models were used to estimate ET0 amounts in 10 different combinations. According to the results obtained, all four methods used predicted ET0 amounts in acceptable accuracy and error levels. BFGS-ANN model showed higher success than the others. In kernel-based GPR and SVR methods, Pearson VII function-based universal kernel was the most successful kernel function. Besides, the scenario that is related to temperature in all scenarios used, including average temperature, maximum and minimum temperature, and sunshine duration gave the best results. The second-best scenario was the one that covers only the sunshine duration. In this case, the ANN (BFGS-ANN) model, which is optimized with the BFGS method that uses only the sunshine duration, can be estimated with the 0.971 correlation coefficient of ET0 without the need for other meteorological parameters.

Mohammad Taghi Sattari et al.

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Mohammad Taghi Sattari et al.

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Short summary
The aim of study is to estimate the reference evapotranspiration (ET0) amount with artificial intelligence by minimum meteorological parameters in the Corum region which is an agricultural center of Turkey. GPR and SVR kernel-based, 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 in acceptable accuracy and error levels. BFGS-ANN model showed higher success than the others.
The aim of study is to estimate the reference evapotranspiration (ET0) amount with artificial...
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