One of the most important processes of erosion and sediment transport in streams is the river most complex engineering  issues.this process special effects on water quality indices, action suburbs floor and destroyed much damage to the river and also into the development plans  Lack of continuity sediment sampling and measurement of many existing stations. due to the low number of hydrometric stations in Iran and the lack of continuity of sediment sampling and measuring in many existing stations, on one hand the exact amount of sediment load in many rivers in the country is not available and because of differences in climatic, hydrological and topographical conditions in the country, on the other hand, the preparation and calibration of sediment Erosion Models different regions, is unavoidableCalibration models of erosion and sedimentation in different locations is difficult and requires financial capital andthe time . the But evolutionary optimization algorithm able to resolve this problems of mathematical and experimental methods in this paper, a new optimization algorithm spiders can be made to education And the evolutionary pattern for input (discharge and precipitation) and rain-gauge gauging stations and Watershed Kardeh designated evolutionary algorithms and artificial network performance for 24 year 24-year dam catchment Kardeh for the period studied. In conclusion, the results proved that social spiders optimization algorithm t better resultspredic to for sediment in watershed Kardeh

The purpose of this study was to estimate the amount of sediment of Vanai River in Borujerd. In this research, the characteristics of the sub-basins of this river have been extracted first. These specifications include the physical characteristics of the sub-basins, including the area, the environment and length of the waterways, and the characteristics of the river flow, and its sediment content. In the following, multivariate linear regression, multilevel prefabricated neural network (MLP) and radial function-based neural network (RBF) models are used to model sediment estimation. After estimating the model, the mean square error index (RMSE) was used to compare the models and select the best model. Evidence has shown that initially the MLP's neural network model had the best estimate with the lowest error rate (90.44) and then the RBF model (151.44) among the three models. The linear regression model has the highest error rate because only linear relationships between variables are considered.