Estimating groundwater salinity is important for the use of groundwater resources for irrigation purposes and provides a suitable guide for the management of groundwater. In this study, the artificial neural networks (ANNs) were adopted to estimate the salinity of groundwater identified by total dissolved solids (TDS), sodium adsorption ratio (SAR), and sodium (Na+) percent, using electrical conductivity, magnesium (Mg2+), calcium (Ca2+), potassium (K+), and potential of hydrogen (pH) as input elements. Samples of groundwater were brought from 51 wells situated in the plateau of Najaf–Kerbala provinces. The network structure was designed as 6-4-3 and adopted the default scaled conjugate gradient algorithm for training using SPSS V24 software. It was observed that the proposed model with four neurons was exact in estimating the irrigation salinity. It has shown a suitable agreement between experimental and ANN values of irrigation salinity indices for training and testing datasets based on statistical indicators of the relative mean error and determination coefficient R 2 between ANN outputs and experimental data. TDS, SAR, and Na percent predicted output tracked the measured data with an R 2 of 0.96, 0.97, and 0.96 with relative error of 0.038, 0.014, and 0.021, respectively, for testing, and R 2 of 0.95, 0.96, and 0.96 with relative error of 0.053, 0.065, and 0.133, respectively, for training. This is an indication that the designed network was satisfactory. The model could be utilized for new data to predict the groundwater salinity for irrigation purposes at the Najaf–Kerbala plateau in Iraq.

An aqueducts are a water source (the channel that a flowing body of water follows) designed to transport water from a specified point to a point where the designer aims to distribute the water within it. To enhance the hydraulic properties of pipe aqueducts, a workable, efficacious, and convenient method for the optimal design of an aqueduct has been determined in this research article to study the optimum design of pipe aqueduct (finding optimum diameter) and study the effect of design parameters on safe span length by MATLAB Software R2017b and Newton–Raphson method and check the effects of the parameters of design such as the span length (L), discharge (Q), overhead loss (H), inlet and outlet coefficient ( K K 1 & K K 2), etc. Also, this article studies the safe span (L) depending on the optimum value of pipe diameter.