Reference Evapotranspiration Estimation Using Adaptive Neuro-Fuzzy Inference System at Shahat in Libya
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Reference Evapotranspiration, FAO Penman-Monteith, Machine Learning Techniques, Adaptive Neuro-Fuzzy , Fuzzy Inference System
Abstract
This study was conducted to estimate the reference evapotranspiration (ETo) for Shahat region in Libya using an adaptive neuro-fuzzy inference system (ANFIS) compared to the FAO Penman-Monteith equation (FPM56). The climate data series of Shahat Meteorological Station was used for the time period between 1963 and 1999. Six combinations of these climate data were used as inputs to the ANFIS model. These combinations are composed of mean temperature (Tmean), mean relative humidity (RHmean), and extraterrestrial radiation (Ra), the latter is calculated value from location and time of the year. The ANFIS model was trained using 70% of the data, and the remaining part of the data was divided into two halves, 15% for validation phase and 15% for testing phase. The results of this study showed that the value of the root mean square error (RMSE) ranged between 0.32-0.96 (mm.d-1) and the value of determination coefficient (R2) ranged between 0.7-0.98 during the testing phase. This study confirmed the fact that the (ANFIS) technique is an accurate method for estimating ETo, especially in the absence of complete climate data.
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