Extracting a Set of Robust Pareto-Optimal Parameters for Hydrologic Models using NSGA-II and SCEM

In this paper, we will present a heuristic method in order to combine the information about the parametric space of a conceptual hydrologic model from two different sources. On one hand, multi-objective evolutionary optimization algorithm NSGA-II is used to find a set of pareto optimal solutions. On the other hand, a Markov Chain Monte Carlo-based algorithm, i.e. Shuffled Complex Evolution Metropolis (SCEM) is used to highlight a set of parameters with higher posterior distribution. By covering the interval between the most crowded locations in the parametric space extracted by both algorithms, we will identify a set of pareto optimal solutions which is more robust than the initial non-dominated set extracted by only NSGA-II.