Author(s): Li Li, Eun-Sung Chung, Kyung Soo Jun
Linked Author(s): li LI
Keywords: Minimax regret approach, Pareto optimum, robust parameter set selection, unsteady flow model, variable roughness coefficients
Abstract: A robust parameter set (ROPS) selection method for a hydrodynamic flow model is developed by combining Pareto optimums obtained by outcomes of multi-site calibration problem based on the observations of multi-sites with the minimax regret approach (MRA). The multi-site calibration problem which is a multi-objective problem is solved by using an aggregation approach which aggregates the weighted criteria related to different sites into one measure, and then performs a large number of individual optimization runs with different weight combinations. Roughness parameter structure which can describe the variation of Manning's n with discharges and sub-reaches was developed and the related coefficients are optimized as model parameters by minimizing the sum of the squares of the weighted residuals between the computed and observed water levels at two gauging stations. Different combinations of weights on the observations at two sites are considered for the aggregation approach to solve multi-objective function, and the corresponding optimized parameter sets (Pareto optimums) are assumed as the ROPS candidates. One flood event is selected for calibration and another one is chosen for validation, where the two events differ significantly in their rainfall distributions. In the application of MRA, which is a decision criterion, the Pareto solutions are ranked based on the obtained regrets related to each Pareto solution, and the top-rated one due to the lowest aggregated regrets of both calibration and validation is determined as the only ROPS. It is found that the determination of variable roughness and the corresponding standardized RMSEs at the two gauging stations vary considerably depending on the combinations of weights on the two sites. This method can provide the robust parameter set for the multi-site calibration problems in hydrologic and hydraulic models
Year: 2017