Mohammad Golshan; Abazar Esmaili; ali afzali; Afshin Jahanshahi
Abstract
Simulation with using of computer models are developing very fast and these models are essential tools for understanding of man from the watershed and hydrological processes. In this study IHACRES and HEC-HMS rainfall runoff models was used for simulation of four flood hydrographs in Kasilian watershed ...
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Simulation with using of computer models are developing very fast and these models are essential tools for understanding of man from the watershed and hydrological processes. In this study IHACRES and HEC-HMS rainfall runoff models was used for simulation of four flood hydrographs in Kasilian watershed with area 67.8 square kilometers in Mazandaran province. To prepare the Requirements maps for running HEC-HMS model was used HEC-GeoHMS extension. Hyetograph data with 15 minutes time step and its related hydrograph was entered to two models based on 1 hour time step. Then simulation of the flood hydrograph was done based on 15 minutes time step. CP and RE% statistical coefficients was used for evaluation of models performance. Values of this coefficients using the HEC-HMS model for flood 26 November 1994 was calculated 0.72 and 118.26 respectively and for flood 6 October 1996, 0.89 and -24.63 respectively and using the implementation of IHACRES model these coefficients in the first flood was calculated 0.63 and 152.4 respectively and in the second flood 0.79 and -35.6 respectively. The results showed that both use models have acceptable performance for simulation of flood hydrograph in this area and HEC-HMS model has better performance in compared with IHACRES model.
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.