In this research, Artificial Neural Networks (ANNs) technique was applied in an attempt to predict the water levels and some of the water quality parameters at Tigris River in Wasit Government for five different sites. These predictions are useful in the planning, management, evaluation of the water resources in the area. Spatial data along a river system or area at different locations in a catchment area usually have missing measurements, hence an accurate prediction. model to fill these missing values is essential.
The selected sites for water quality data prediction were Sewera, Numania , Kut u/s, Kut d/s, Garaf observation sites. In these five sites models were built for prediction of the water level and water quality parameters.

339 individuals , were sorted from 22 samples collected from three sites in Tigris River including , Al- Sarafiya district (S1), Al- Jaderiyah district (S2) and Al-Za'afaraniya district (S3), in addition to one site in the irrigation canal of the Al- Jaderiyah campus of the University of Baghdad (S4) , and in Al- Jeish canal(S5) east Baghdad. The sorting results revealed that the highest number of individuals of 102 was recorded at S4, whereas the lowest number of 24 individuals was recorded at S2. Regarding the sites, site S4 was the richest site with 30% of the total number represented 16 species, while each of S3 and S5 had 8 species only with 17.11% and 28.60% of the total individuals number respectively. The values of Jaccared Sim

Benthic algae of Tigris river and one of its northern tributary the lower Zab were study at monthly intervals during Nov. 2001-Oct. 2002. Four sites were selected, a total of 115 species of algae were identified during this study, diatoms was the dominating group (86 species) followed by Chlorophyta (18 species), Cyanophyta (7species), Euglenophyta (2 species) and one species for each of Pyrrophyta and Chryzophyta. Pennate diatoms formed the major density within the identified algae and distributed among all stations especially the species Achnanthes minutissima, Navicula gracilis and Nitzschia palea, the diatoms bloomed in spring and autumn seasons. Bio-diversity and density of benthic algae in Tigris river was affected negatively by the e

Baghdad Metro is a vital project to fulfill the rapidly increased traffic volume requirements. The proposed metro will connect both sides of Baghdad City, passing under the Tigris River. This study is employed finite elements software (PLAXIS 3D) to evaluate the seepage force developed around the sub-river segment during different construction stages and for other water levels of Tigris. The study found that when the water level changes from maximum to minimum, the developed seepage force decreases by (8 to 13%) and (22 to 27%) respectively. The seepage forces were found to be maximum during the excavation stage. The concrete lining process led to a noticeable reduction in seepage forces at all locations. The study also

The present work included qualitative study of epiphytic algae on dead and living stems, leaves of the aquatic plant Phragmitesaustralis Trin ex Stand, in Tigris River in AL- Jadria Site in Baghdad during Autumn 2014, Winter 2015, Spring 2015, and Summer 2015. The physical and chemical parameters of River’s water were studied (water temperature, pH, electric conductivity, Salinity, TSS, TDS, turbidity, light intensity, dissolve oxygen, BOD5, alkalinity, total hardness, calcium, magnesium and plant nutrient). A total of 142 isolates of epiphytic algae were identified. Diatoms were dominant by 117 isolates followed by Cyanobacteria (13isolates), Chlorophyta (11 isolates) and Rhodophyta (1 isolate), Variations in the isolates number were rec

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