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Achieving high precision results in case of low organic matter and high chloride concentration can only be possible by both decreasing the concentration of oxidant and adding mercury sulphate.

The amount of sediment should be taken into consideration in the planning of water structures for efficient use of limited water resources. It is important to estimate the amount of sediment for the successful operation of these structures in their future performances. Such estimations can be achieved by Artificial Neural Network (ANNs) with low error percentages as seen in many other disciplines. These networks also enable the modeling of nonlinear relationships between the parameters affecting the event. The purpose of this research is to establish models for sediment amounts in the Tigris River at the Diyarbakir measurement station in Turkey. Rainfall, temperature and discharge are taken as independent variables in the models, whereas sediment is taken as the dependent variable. Fourteen different models are generated using ANNs and Regression Analysis (RA). The results are compared with each other and with the observed data. The relative error and determination coefficient are used as comparison criteria. It is concluded that due to their nonlinear modeling capability, ANNs give better results than RA.

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