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

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.