Calibrating the most appropriate parameter is one of the crucial stages in dependable rainfall-runoff modeling. This paper evaluates the computational performance of Ant Colony Optimization (ACO) and Marquardt Algorithms (MA) used for calibration of CN and λ parameters of the Soil Conservation Service Curve Number (SCS-CN) model in optimization. Employing standard error of estimate (Se) and standard error ratio (SER), the performance of both the algorithms was tested on a large set of US data.
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