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ANALYZING OF REFERENCE EVAPOTRANSPIRATION USING EXTREME LEARNING MACHINE APPROACH
Abstract
Accurate prediction of reference evapotranspiration (ET0) is essential in water resources planning and management of irrigation systems. The ET0was determinate using the FAO-56 Penman-Monteith equation based on the weather data collected in Serbia during the period 1980-2010. The purpose of this research is to develop and apply the Extreme Learning Machine (ELM) to estimate and calculate the ET0. The process was implemented for eight input combinations in order to find the most optimal input combination for ET0 prediction. Primary objective of the current study is to evaluate the results of ELM for ET0 prediction for eight input combinations in order to find the most optimal input combination for ET0 prediction. The reliability of the computational model was accessed based on simulation results and using two statistical tests including coefficient of determination and root-mean-square error. Based upon simulation results, it is demonstrated that ELM can be utilized effectively in applications of ET0 predictions. The results could be also used as the benchmark for the future investigation into the reference evapotranspiration.
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