A. Salajegheh; A. Fathabadi
Volume 62, Issue 2 , October 2009, , Pages 271-282
Abstract
Correct estimation of suspended sediment transported by a river is an important practice in water structure design, environmental problems and water quality issues. Conventionally, sediment rating curve used for suspended sediment estimation in rivers. In this method discharge and sediment discharge ...
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Correct estimation of suspended sediment transported by a river is an important practice in water structure design, environmental problems and water quality issues. Conventionally, sediment rating curve used for suspended sediment estimation in rivers. In this method discharge and sediment discharge or concentration related using regression relation that generally is exponential model. Respect to uncertainty and nonlinear relation between discharge and sediment concentration, sediment rating curve has not enough efficiency for this purpose. In this study using Artificial Intelligent (Fuzzy Logic and Artificial Neural Network), suspended sediment in Karaj River was estimated. First, various neural network and fuzzy logic models established. For neural network and fuzzy logic, models with four neuron in hidden layers and FIS (Fuzzy Inference System) with four Gaussian membership functions, respectively were selected as the best structure. Finally, the results showed that fuzzy logic estimates the suspended sediment loud better than the other techniques and therefore is suggested for estimation of suspended sediment load.
A. Salajegheh; A. Fathabadi; M. Mahdavi
Volume 62, Issue 1 , June 2009
Abstract
Rainfall-runoff is one of complex hydrological processes that is affected by a variety of physical and hydrological factors. In this study statistical method ARMAX model, neural network, neuro-fuzzy (ANFIS subtractive clustering and grid partition) and two hybrid models of this methods were used to simulate ...
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Rainfall-runoff is one of complex hydrological processes that is affected by a variety of physical and hydrological factors. In this study statistical method ARMAX model, neural network, neuro-fuzzy (ANFIS subtractive clustering and grid partition) and two hybrid models of this methods were used to simulate rainfall-runoff and prediction of streamflow. In each method optimum structure was determined then, streamflow forecasted using the best model. The results showed that hybrid methods have better application than single models and artificial intelligent has better application than linear ARMAX model due to nonlinearity of rainfall-runoff process. In this study all methods showed relatively suitable application but ANFIS method with subtractive clustering is suggested for modeling rainfall-runoff and streamflow prediction.
