Hybrid of Artificial Neural Network-Genetic Algorithm for Prediction of Reference Evapotranspiration (ET?) in Arid and Semiarid Regions

Evapotranspiration is a principal requirement in designing any irrigation project, especially in arid and semiarid regions. Precise prediction of Evapotranspiration would reduce the squandering of huge quantities of water. Feedforward Backpropagation Neural Network (FFBPNN) model is employed in this study to evaluate the performance of Artificial Neural Networks (ANNs) in comparison with Empirical FAO Penman-Monteith (P-M) Equation in predicting reference evapotranspiration (ETo); later, a hybrid model of ANN-Genetic Algorithm (GA) is proposed for the same evaluation function. Daily averages of maximum air temperature (Tmax), minimum air temperature (Tmin), relative humidity (Rh), radiation hours (R), and wind speed (U2) from Mosul station (Nineveh, Iraq) are used as inputs to the ANN simulation model to predict ET? values obtained using P-M Equation. The main performance evaluation functions for both models are the Mean Square Errors (MSE) and the Correlation Coefficient (R2). Both models yield promising results, but the hybrid model shows a higher efficiency in prediction of Evapotranspiration and could be recommended for modeling ET? in arid and semiarid regions.