Determine the Most Appropriate Method for Estimating Suspended Sediment in the River, Case Study, Garm Roud River, Mazandaran Province, Iran

Simulation and evaluation of river sediment is important in water resources management. The common ways of measuring the sediment concentration are generally time consuming and costly. Moreover, they sometimes do not possess sufficient accuracy. In this study, the sediment of the Garm Roud river was estimated using artificial neural network and the results were compared via common statistical procedures e.g., multivariate linear regression. River discharge parameter on a monthly time scale in the period (1990 to 2001) and the sedimentation rate were taken as input and output, respectively. Correlation coefficient, root mean square error and Nash-Sutcliffe coefficient were utilized to evaluate and compare the performance of models. The results showed that both models used to estimate discharge had acceptable accuracy, but the ANN model with the highest correlation (0.894), minimum root mean square error (0.062 ton/day) and Nash Sutcliffe coefficient of 0.756 was prioritized in the verification phase. The results showed that the artificial neural network had a high ability to estimate the minimum and maximum values of sedimentation rate.
International Bulletin of Water Resources and Development, Volume:1 Issue: 3, 2014
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