Prediction of wind drift and evaporation losses in sprinkler irrigation systems using artificial neural networks

Wind drift and evaporation loss (WDEL) in sprinkler irrigation systems are a factor that affects the water delivery in a field and reduces the uniformity of spraying. So, predicting these losses can play an important role in improving the performance of them. In this study, artificial neural networks (ANNs) have been used to estimate the hourly sprinkler discharge efficiency (SDE), which, in turn, is dependent on WDEL. The effective parameters in estimating WDEL, which were considered as model inputs, were selected by calculating Spearman rank correlation coefficients. Accordingly, wind speed, temperature, relative humidity, and reference evapotranspiration were considered as model inputs, whereas SDE was considered as model output. The performance evaluation of the developed neural network model was done using 1024 real data obtained by the Strip structure for estimating WDEL. The proposed model, which was a 4-19-16-1 model with a Bayesian regularization training function, was selected upon testing 3780 different neural networks. The results of this study showed that the developed model can accurately estimate the hourly values of sprinkler discharge efficiency (R= 0.84, RMSE= 1.6%, MAPE= 1.19) and can be used as a reliable method in evaluating the performance of sprinkler irrigation systems.