Evaluation of Intelligent Data-Driven Models in Daily Reference Evapotranspiration Estimation in Climates of Southern Coasts of the Caspian Sea

Document Type : Original Article

Authors

1 Assistant Professor, Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran

2 M. Sc. Student of Water Resources Engineering, Department of Water Engineering, College of Agriculture, Shahid Bahonar University of Kerman., Kerman., Iran

3 Assistant Professor, Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman Iran

Abstract

In many studies accurate estimation of evapotranspiration parameter has special importance. Intelligent data-driven models have great potential in the modeling of complex and nonlinear phenomena. In this study reference evapotranspiration was  modeled by using of four data-driven method including Artificial Neural Networks (ANN), Support Vector Machines (SVM), model tree M5 and Adaptive Networks based on Fuzzy Inference System (ANFIS) with Long-term daily meteorological data in three synoptic stations of Climates of southern coast of the Caspian Sea. 11 different combinations of meteorological variables was chosen as input data-driven models and for evaluate the models many statistical were used such as correlation coefficient (R2), Root Mean Square of Error (RMSE) and index of agreement (DI). The Result that showed ANFIS-11 model  Was provided best estimations with RMSE between 0.2 ~ 0.38 mm day-1 and models with single-input wind speed was presented poorest estimations with RMSE between 1.35 ~ 1.68 mm day-1 and R2 between 0.02 ~ 1.68 in climates studied.

Keywords


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