Luay Kadhim Hameed Al-Waeli

Developing and assessing the performance of water projects and irrigation networks is based on many factors, such as flow measurement. Weir and gate structures have been extensively utilized for flow measurement and to get rid of sediments. The process of modeling and estimating the coefficient of discharge in the weir is an essential part of hydraulic engineering. Recently the application of computer skills was adopted instead of traditional methods. In the present study, the artificial neural network (ANN) was adopted to estimate the coefficient of discharge for a combined weir that consisted of the trapezoidal weir and rectangular gate. For this purpose, the experimental data were collected and analyzed. The dimensional analysis was used to identify the effective dimensionless parameters related to the discharge coefficient. The developed ANN network structure was designed as 6-10-1 and adopted the default scaled conjugate gradient algorithm for training using SPSS V24 software. It was found that the proposed model with ten neurons was highly accurate in predicting the discharge coefficient. The sensitivity analysis was adopted to assess the performance of the ANN using different numbers of effective input parameters. Assessing five models, the ratio of upstream head to gate height (H/d), slope of trapezoidal angle (tan θ), and the ratio of distance between weir and gate and gate height (y/d) parameters are adequate for estimating the discharge coefficient compared to other parameters. ANN model with input parameters of H/d, h/d (h is the flow depth over the trapezoidal weir), b g /d (b g is the gate width),tanθ, b g /b (b is the tota

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),andsodium(Na ) percent, using electrical conductivity, magnesium (Mg + 2+ ), calcium (Ca 2+ ),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 6-4-3and 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 experimentaldata.TDS,SAR,andNapercentpredicted output tracked the measured data with an R of 0.96, 0.97, and 0.96 with relative error of 0.038, 0.014, and 0.021, respectively,fortesting,andR 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

A Hydraulic analysis was performed to estimate sediment transport rates for the Euphrates River at a specified reach in (Al- Hindiya city). Information presented in this study includes surveyed channel cross sections, discharge, stages, and measurements of bed material characteristics. The study reach was between station (Km 625+200) upstream and station (Km 639+200) downstream with a length of (14) km. This reach includes (20) cross sections. HEC-RAS (4.1) package was used and applied for modeling and calculating the sediment transport by (Ackers-white, Engelund-Hansen, Laursen, Toffaleti, and Yang) formulas. Hydraulic computations existing in HEC-RAS4 were used to compute a series of steady flow profiles (13 profiles).These profiles used to develop hydro-dynamic parameters for sediment transport and calibrated to the hydraulic conditions of the reach. A stable solution for sediment transport rates was obtained under specified conditions. The modeling formulas were compared with the field results and the closest formula to the field results was (Laursen), while (Ackers-White) formula was the poor one while the other samples give lower results than the field values with a bit of convergence in some of these results which had been obtained through statistical method. The average annual sediment transport has been predicted by (Laursen) formula using HEC-RAS (4.1) model to be (3376) tons.

The appropriate value of Manning’s roughness coefficient (n) is chosen through the process of calibration; i.e., the value which reproduces observed data to an acceptable accuracy. In the present study, the HEC-RAS unsteady flow model is applied to Hilla river (upstream Hilla city) to predict the value of Manning’s coefficient through the calibration procedure. The data are taken for the period from 20 August 2008 to 12 September 2008 and divided equally into two sets; the first set is for calibration purpose; i.e., estimation of (n) and the rest for verification which is the process of testing the model with actual data to establish its predictive accuracy. It is found that the value of Manning's roughness coefficient (n) for Hilla river which shows good agreement between observed and computed hydrographs is (0.027).

Based on the recommendations of the reviewers and the Scientific Committee, we are delighted to inform you that your manuscript entitled "Hydrodynamic Based Model to Simulate Water Quality in Kufa River, Iraq" has been accepted to be published in the 4th International Conference on Engineering and Advanced Technologies (ICEAT-2025) which will be held on July 23-24, 2025, in Petaling Jaya, Selangor, Malaysia. The accepted manuscripts will be published in the EPSTEM publisher conference proceedings, which is Scopus indexed.

the arti?cial neural network (ANN) was adopted to estimate the coef?cient of discharge for a combined weir that consisted of the trapezoidal weir and rectangular gate

the arti?cial neural networks (ANNs) were adopted to estimate the salinity of groundwater identi?ed by total dissolved solids (TDS), sodium adsorption ratio (SAR),andsodium(Na ) percent, using electrical conductivity, magnesium (Mg + 2+ ), calcium (Ca 2+ ),potassium(K + ), and potential of hydrogen (pH) as input element