The need to create the optimal water quality management process has motivated researchers to pursue prediction modeling development. One of the widely important forecasting models is the sessional autoregressive integrated moving average (SARIMA) model. In the present study, a SARIMA model was developed in R software to fit a time series data of monthly fluoride content collected from six stations on Tigris River for the period from 2004 to 2014. The adequate SARIMA model that has the least Akaike's information criterion (AIC) and mean squared error (MSE) was found to be SARIMA (2,0,0) (0,1,1). The model parameters were identified and diagnosed to derive the forecasting equations at each selected location. The correlation coefficien

Benthic   invertebrates'   diversity   and    some  physical    a:1d

chemical characteristics in Lower Zab tributary and Tigris River were studied.  Month l y samples  were col l ected  from  November  2001 to October 2002.

The rt:sLllts of the present study showed  the turbidity iu Tigris river

before the confluence to be higher (34.4 NTU) compared to the other stations.  Mean salinity was greater in Lower Zab tributary (0.33 %) compared to that in the river.  Lower Zab tJibutary and Tigris River were good in Oxygen content, and a high dissolved oxygen value was recorded (7.9 mg/L) in Lower Zab tributary.

The current study aimed to determine the morphometric and meristic characteristics of the North African catfish Clarias gariepinus (Burchell, 1822). Six specimens of C. gariepinus were collected from the Tigris River, in central Iraq. This study is considered the confirmation first record of this species in Iraq, and the second documentation of this exotic fish. The present species is characterized by a very long dorsal fin, a rounded caudal fin and four pairs of barbels.

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.

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

LandSat Satellite ETM+ image have been analyzed to detect the different depths of regions inside the Tigris river in order to detect the regions that need to remove sedimentation in Baghdad in Iraq Country. The scene consisted of six bands (without the thermal band), It was captured in March ٢٠٠١. The variance in depth is determined by applying the rationing technique on the bands ٣ and ٥. GIS ٩. ١ program is used to apply the rationing technique and determined the results.