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Reference evapotranspiration  ET o   is one of the most significant factors in the hydrological cycle since it has a great influence on water resource planning and management, agriculture and irrigation management, and other processes in the hydrological sector. In this study, an efficient and local predictive model was established to forecast the monthly mean  ET o   t over Turkey based on the data collected from 35 locations. For this purpose, twenty input combinations including hydrological and geographical parameters were introduced to three different approaches called multiple linear regression  MLR , random forest  RF , and extreme learning machine  ELM . Moreover, in this study, large investigation was done, involving the establishment of 60 models and their assessment using ten statistical measures. The outcome of this study revealed that the ELM approach achieved high accurate estimation in accordance with the Penman–Monteith formula as compared to other models such as  MLR and  RF . Moreover, among the 10 statistical measures, the uncertainty at 95%  U 95 indicator showed an excellent ability to select the best and most efficient forecast model. The superiority of  ELM in the prediction of mean monthly  ET o   over  MLR and  RF approaches is illustrated in the reduction of the  U 95 parameter to 49.02% and 34.07% for  RF and  MLR models, respectively. Furthermore, it is possible to develop a local predictive model with the help of computer to estimate the  ET o   using the simplest and cheapest meteorological and geographical variables with acceptable accuracy.

Application of Artificial Intelligence Models for Evapotranspiration Prediction along the Southern Coast of Turkey