Abstract
The calibration of conceptual rainfall runoff (CRR) models is an optimization problem whose objective is to determine the values of the model parameters which provide the best fit between observed and estimated flows. This study investigated the performance of three probabilistic optimization techniques for calibrating the Tank model, a hydrologic model typical of CRR models. These methods were the Shuffled Complex Evolution (SCE), genetic algorithms (GA) and simulated annealing (SA) methods. It was found that performances depended on the choice of the objective function considered and also an the position of the start of the optimization search relative to the global optimum. Of the three global optimization methods (GOM) in the study, the SCE method provided better estimates of the optimal solution than the GA and SA methods. Regarding the efficiency of the GOMs, as expressed by the number of iterations for convergence, the ranking in order of decreasing performance was the SCE, the GA and the SA methods.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
