The Diyala River is considered the third most important river in Iraq. However, in the recent period, Diyala Governorate has been subject to several floods. This study aims to simulate an efficient labyrinth weir at the flood escape entrance branching from the Diyala River to reach the best entrance through which the flood waves can pass safely. The discharge coefficient was calculated laboratory for five types of trapezoidal side labyrinth weirs with different sidewall angles. Results showed that the coefficient discharge for the trapezoidal labyrinth side weir with an angle of the sidewall is 75ᵒ and has a discharge coefficient greater than the rest of the labyrinth side weirs. The second part of this study is valida

This study aims to assess the water quality index (WQI) according to the Canadian Council of Ministers of the Environment's Water Quality Index method (CCME WQI). Four locations (measurement stations) are selected along the Tigris River, in Iraq.  Two of them are located in the north near Mosul City, (Mosul Dam and Mosul city), and the other two are located in the south near Al-Amarah city, (Ali Garbi and Al-Amarah). The water data collected is for the period 2011 to 2013, including eleven water quality parameters. These are magnesium (Mg⁺²), calcium (Ca²⁺), potassium (K⁺), sodium (Na⁺), sulfate (SO₄^2-), chloride (Cl^-), nitrate (NO₃<

Total dissolved solids are at the top of the parameters list of water quality that requires investigations for planning and management, especially for irrigation and drinking purposes. If the quality of water is sufficiently predictable, then appropriate management is possible. In the current study, Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were used as indicators of water quality and for the prediction of Total Dissolved Solids (TDS) along the Tigris River, in Baghdad city. To build these models five water parameters were selected from the intakes of four water treatment plants on the Tigris River, for the period between 2013 and 2017. The selected water parameters were Total Dissolved Solids (TDS

The study aims to predict Total Dissolved Solids (TDS) as a water quality indicator parameter at spatial and temporal distribution of the Tigris River, Iraq by using Artificial Neural Network (ANN) model. This study was conducted on this river between Mosul and Amarah in Iraq on five positions stretching along the river for the period from 2001to 2011. In the ANNs model calibration, a computer program of multiple linear regressions is used to obtain a set of coefficient for a linear model. The input parameters of the ANNs model were the discharge of the Tigris River, the year, the month and the distance of the sampling stations from upstream of the river. The sensitivity analysis indicated that the distance and discharge

The determination of river pollution impact on the performance of water treatment plants is achieved by two main objectives. The first is to study raw and treated water qualities and comparing them with standards and the second is to evaluate the treatment plants efficiency. The analyzed data were those water quality parameters in relation to physical, chemical and bacteriological characteristics for river water and produced water by seven water treatment plants located on Tigris River passing through Baghdad City.
The results of this study indicated that all raw water characteristic are within the surface water standards established by Iraqi and USA criteria except Bacterial Counts.
Tigris River water is of good quality to be trea

Segmented regression consists of several sections separated by different points of membership, showing the heterogeneity arising from the process of separating the segments within the research sample. This research is concerned with estimating the location of the change point between segments and estimating model parameters, and proposing a robust estimation method and compare it with some other methods that used in the segmented regression. One of the traditional methods (Muggeo method) has been used to find the maximum likelihood estimator in an iterative approach for the model and the change point as well. Moreover, a robust estimation method (IRW method) has used which depends on the use of the robust M-estimator technique in