Simulation of suspended sediment load using neuro-fuzzy fusion model with different input combinations (case study: Sierra station-Karaj dam)

In order to reduce sediment and soil conservation plan generation، the design and calculation of the exact size of the dam at dams، evaluation and estimation of sediment yield in the watershed of the dam، is essential. In general erosion and sediment transport of the most complex issues that determine the exact hydrodynamic equations for the various variables، it is not easy. The aim of this study was to simulate suspended sediment load using a neural-fuzzy model is combined This is necessary for the various input combinations using stepwise regression، genetic algorithm model and gamma test and then determine the most suitable model for the input. Karaj Dam Sierra station data in the period 1974-2011 (37 years) was used so that the 15 variables as input and output variables as was considered. Results indicated neuro-fuzzy models in a suitable combination of the number of training data to select the most appropriate modeling of the genetic algorithm to be achieved. It should be noted that in the case of all input variables، modeling neuro-fuzzy model was not appropriate Therefore، methods are necessary to determine the appropriate combination of inputs. Finally، the Genetic Algorithm model as the most appropriate method and stepwise regression، second، and third order gamma test as appropriate methods to determine combination of neuro-fuzzy model inputs selected. The neuro-fuzzy model when using a selected combination of genetic algorithms to simulate suspended sediment load 99/0 correlation coefficient and the mean square error as the most appropriate modeling 0000009/0 has been selected.