عنوان مقاله [English]
Estimation of sediment concentration in rivers is very important for water resource projects planning and managements. Various models have been developed so far to identify the relation between discharge and sediment load. One of the most common methods for estimating sediment of rivers is sediment rating curve. For better estimation of the amount of sediment based on the sediment curve rating equation, it is possible to optimize its coefficients. One of the methods used for optimizing the coefficients of the sediment curve rating equation is taking advantage of meta-heuristic algorithms. Nowadays, optimization algorithms are used regularly for solving complex and non-linear problem. The main objective of this research is the use of genetic algorithms and particle swarm to optimize the relationship between discharge and sediment discharge in Kohak station on the Sistan River and comparison the results of these models with sediment rating curve. For the calculation of sediment discharge by the models initially necessary statistics and information including flow discharge and sediment concentrations have been measured since 1981-2011 in the stations are collected. Genetic Algorithm(GA) and Particle Swarm Algorithm(PSO) models were coded in MATLAB. After the models were trained with 70% to 30% of the data at both stations were tested. Evaluation parameters efficiency such as coefficient of determination (R2), root mean square error (RMSE) and Nash-Sutcliffe coefficients (CE) are used in evaluating the models. The results showed that the genetic algorithm with 33484.47 values at Kohak station has lowest root mean square error in all models. After genetic algorithm, Particle swarm algorithm with 34754.31 values has lowest values of the objective function.