Applicability evaluation of different algorithms for daily reference evapotranspiration model in KBE system

Abstract

The irrigation decision-making system based on knowledge-based engineering (KBE) is reported in this paper, and the basis of the KBE was knowledge of reference crop evapotranspiration (ET0). Therefore, the research examined the accuracy of the support vector machines (SVMs) in the model of ET0. In the first part of the study, the SVMs were compared with FAO-24, Hargreaves, McCloud, Priestley-Taylor and Makkink models. The results indicated that the SVMs performed better than other models. In the second part, the total ET0 estimation of the SVMs was compared with the other models in the validation. It was found that the SVMs models were superior to the others in terms of relative error. The further assessment of SVMs was conducted, and confirmed that the models could provide a powerful tool in KBE irrigation with a lack of meteorological data. It could provide a reference for accurate ET0 estimation in KBE irrigation systems.