Suitable design and management of drip irrigation systems depends on an accurate knowledge of the moisture distribution patterns around the emitters. An important parameter in the design of surface and subsurface dripirrigation systems is the wetted pattern of soil area in the up and down wetted areas of drippers. Accurate estimation of the up and down wetted areas in surface/subsurface irrigation systems is essential for enhancing the efficiency of irrigation systems and optimal management of water resources in the field. In this study, artificial neural network (ANN) and nonlinear regression (NLR) methods were used to develop equations for the estimation of the up and down wetted areas around the dripper installation position. Experiments were carried out in a transparent physical Plexiglas with dimensions of 3 m 1.22 m 0.5 m × × . In this study, three different soil textures (i.e., light, medium, and heavy) were used. The drippers at four installation depths, including 0, 15, 30, and 45 cm, were evaluated. By considering an irrigation time equal to 6 h, the emitter discharge rates (i.e., 2.4, 4, and 6 L/s) were applied. Nine different variables, including soil hydraulic conductivity, emitter discharge, soil apparent density, irrigation application time, dripper installation depth, and the soil texture (i.e. amount of clay, silt and sand)were applied as inputs for the developed NLR and ANN models for estimating the up and down wetted areas of drippers. The comparison results between the measured and simulated values showed that the ANN and NLR models have appropriate performances and that the statistical error indices are within an acceptable range. The use of these models for design goals can be helpful in choosing the accurate distance between laterals and emitters as well as the suitable depth of emitters to minimize water losses via deep percolation in surface/subsurface drip irrigation. Furthermore, the results of the ANN and NLR models were compared with an experimental model (developed based on dimensional analysis (DA)) and confirmed the superiority of the ANN and NLR to DA models.
