Estimation of Wind Drift and Evaporation Losses from Sprinkler Irrigation systemS by Different Data‐Driven Methods

Abstract

AbstractAn accurate estimation of losses created by wind drift and evaporation is necessary for water management in sprinkler irrigation systems. In the current study, three artificial intelligence (AI) methods, namely artificial neural network (ANN), adaptive neuro‐fuzzy inference system (ANFIS) and gene expression programming (GEP), were used to estimate the wind drift and evaporation losses (WDEL) based on influential variables such as operating pressure (P), wind speed (W), nozzle diameter (D) and vapour pressure deficit (es − ea). Field measurements data were applied to training and testing of ANN, ANFIS and GEP models. The results of these AI models were also compared with previous studies. Three statistical metrics, namely coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE), were utilized to assess the performance of the models. The results indicate that the AI models (i.e. ANFIS, ANN and GEP) predict the WDEL more accurately than in previous studies. Moreover, the results show that the GEP model (with RMSE = 1.19% and R2 = 0.97) provides a better estimation of WDEL compared to the ANN model (with RMSE = 2.56% and R2 = 0.87) and ANFIS model (with RMSE = 1.64% and R2 = 0.94). Copyright © 2017 John Wiley & Sons, Ltd.