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

This article proposes a new technique for determining the rate of contamination. First, a generative adversarial neural network (ANN) parallel processing technique is constructed and trained using real and secret images. Then, after the model is stabilized, the real image is passed to the generator. Finally, the generator creates an image that is visually similar to the secret image, thus achieving the same effect as the secret image transmission. Experimental results show that this technique has a good effect on the security of secret information transmission and increases the capacity of information hiding. The metric signal of noise, a structural similarity index measure, was used to determine the success of colour image-hiding t

This study included an analysis of three stations (Al Dora, Al Za'franiya, and Arab Ejbur) chosen to study the Physiochemical and microorganism (Fungi and Bacteria) loud of the Tigris River in the southern section of Baghdad city. The result of this research shows that the highest temperature recorded in summer in Al Za'franiya was 37Co, while the lowest temperature recorded in winter in Al Dora was 9Co. and the value of pH recorded the highest in summer it was 7.9 in Arab Ejbur, and the lowest value was in winter 7.1 in Al Dora regions, While Total Organic Carbon (TOC) shows the highest values found in the summer was 6.7 Mg L-1in Al Za'franiya Samples, and the lowest values were 2.0 Mg L-1in Arab Ejbur during the winter. The more f

  This research presents the results of the tests of the physical, chemical and biological treatments performed on the industrial effluent of Al-Rashid Factory for vegetable oils during January to July, 2010.      Some environmental parameters such as temperature, electrical conductivity(ec), Biological oxygen demand(bod), chemical oxygen demand(cod), total suspended solids(tss), dissolved oxygen(o2) and total dissolved solids(tdc) have been investigated. The study shows that the industrial effluent contains a percent of organic pollution and in different values, and that it is responsible for the (bod), which records (5.15-67)ppm, while the (cod) ranges between (25-170.5)ppm, the (ec) values range between (

Al-Yusifia river was assessed at three sampling stations with study period from Autumn 2010 to the end of Summer 2011. The present investigation was carried out on diversity of fungi and bacteria from Al-Yusifia river, Baghdad city. During the study, a total of 12 fungal genus and 6 bacterial genus were isolated during the year seasons. The dominant fungus at the three stations were Penicillium sp., then Rhizopus and Trichophyton   megninii while the dominant bacteria was Escherichia coli and Klebsiella sp.

            The higher

Surface water samples from different locations within Tigris River's boundaries in Baghdad city have been analyzed for drinking purposes. Correlation coefficients among different parameters were determined. An attempt has been made to develop linear regression equations to predict the concentration of water quality constituents having significant correlation coefficients with electrical conductivity (EC). This study aims to find five regression models produced and validated using electrical conductivity as a predictor to predict total hardness (TH), calcium (Ca), chloride (Cl), sulfate (SO₄), and total dissolved solids (TDS). The five models showed good/excellent prediction ability of the parameters mentioned