Determine the Optimal Level of Water Harvesting at the Jengin Dam Using Intelligent Models

In water construction projects, river engineering, and irrigation and drainage engineering, it is vital to estimate the accurate volume of the sediment transported by rivers. As the sediment transport phenomenon is an immensely complex problem, therefore presenting an appropriate solution for precise evaluation of the suspended load in rivers is tedious and the mathematical models are not also accurate enough to be applied. Nowadays application of artificial intelligence systems has been developed as a novel solution in analysis of water resources problems. In this research, the Adaptive Artificial Neural Network (ANN) and fuzzy models were utilized to determine suspended sediment rate of Jagin River. Correlation coefficient (R2) and Root Mean Square Error (RMSE) are considered the model s assessment criteria. The results show a higher accuracy of fuzzy model assessments in comparison with neural networks and sediment rating curve assessments and According to calculations done by the software Abaqus, and determining the amount of sediment in the dam water level of the lake to remove the code elevation 232.5 meters.