Robust parameter estimation framework of rainfall-runoff model with combining Pareto optimality and composite programming

KwangJai Won, Eun-Sung Chung, and Boram Lee, Seoul National University of Science and Technology, South Korea and Yeonjoo Kim, Korean Environmental Institute, , South Korea


This study developed a robust parameter estimation (ROPE) framework of a rainfall-runoff model to consider multi-events with combining the Pareto optimal parameter sets and a composite (CP) programming, one of multi-criteria decision making (MCDM) approaches. The Pareto optimal parameter sets based on the Nash-Shutcliffe coefficient (NSE) were derived for a rainfall-runoff model with a generic algorithm. Then a robust parameter set among the Pareto optimums was selected using the composite values with NSE and peal flow error with the CP programming. Our case study in a small watershed in South Korea shows that the combined framework between traditional optimization techniques such as the Pareto optimality, and MCDM techniques that are not commonly used for the parameter selection problems of hydrologic models, could be an alternative approach for such parameter selection practices that could consider multiple aspects of model simulations.

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