Sediment rating curve (SRC) is a conventional and a common regression model in estimating
suspended sediment load (SSL) of flow discharge. However, in most cases the data log-transformation
in SRC models causing a bias which underestimates SSL prediction. In this study, using the daily stream
flow and suspended sediment load data from Shalman hydrometric station on Shalmanroud River,
Guilan Province, Iran, SRC equation was derived, and then, using evolutionary algorithms (genetic
algorithm and particle swarm optimization algorithm) it was calibrated again. Worth mentioning,
before model calibration, to increase the generalization power of the models, using self-organizing map
(an unsupervised artificial neural network for data clustering), the data were clustered and then by
data sampling, they were classified into two homogeneous groups (calibration and test data set). The
results showed that evolutionary algorithms are appropriate methods for optimizing coefficients of
SRC model and their results are much more favorable than those of the conventional SRC models or
SRC models corrected by correction factors. So that, the sediment rating curve models calibrated with
evolutionary algorithms, by reducing the RMSE of the test data set of 5754.02 ton day-1
(in the initial
SRC model) to 1681.21 ton day-1
(in the calibrated models by evolutionary algorithms) increased the
accuracy of suspended sediment load estimation at a rate of 4072.81 ton day-1
. In total, using
evolutionary algorithms in calibrating SRC models prevents data log-transformation and use of
correction factors along with increasing in the accuracy of molding results