The coefficient of uniformity (CU), an important parameter in design of irrigation systems, affects the quality and return of investment in irrigation projects significantly, and is a good indicator of water losses. In this paper, a single model was proposed to obtain the CU values in four sprinkler types of ZK30, ZM22, AMBO, and LUXOR. Average wind speed, coarseness index (large and small nozzle diameters), and sprinkler/lateral spacing were used as input parameters to obtain the CU values through employing the artificial neural networks (ANN), neuro-fuzzy grid partitioning (NF-GP), neuro-fuzzy sub-clustering (NF-SC), least square support vector machine (LS-SVM) and gene expression programming (GEP) techniques. The available data set consisted of 294 samples that were used to evaluate the proposed methodology. The applied techniques were assessed through the robust k-fold testing data assignment mode. Based on the results, all the applied models presented good capability in estimating CU. The obtained results revealed that the coarseness index (large nozzle diameter) had the lowest impact on modeling CU is sprinkler irrigation systems.
