Multi-Objective Parameter Calibration and Multi-Attribute Decision-Making: An Application to Conceptual Hydrological Model Calibration

For calibrating the conceptual hydrological models (CHM), the traditional calibration method with a single objective cannot properly measure all the behaviors of the hydrological system. To obtain a successful parameters calibration, in this paper, we propose a multi-objective cultural self-adaptive electromagnetism-like mechanism (MOCSEM) algorithm, which is first implemented in solving the parameters calibration problem of CHM. In this algorithm, a self-adaptive parameter is applied in local search operation for adjusting the values of parameters dynamically. Meanwhile, cultural algorithm (CA) is adopted to keep a good diversity and uniformity of Pareto-optimal solutions (POS). MOCSEM is tested, firstly, by several benchmark test problems. After achieving satisfactory performance on the test problems, a case study is implemented for parameter calibration of a CHM by comparing the properties of POS obtained by the MOCSEM and other methods. Finally, when the optimization problem quickly becomes a decision-making problem because of the multiple objectives in CHM, fuzzy technique for order preference by similarity to an ideal solution method has been used to rank the POS and select the optimal scheme. The results show that the MOCSEM algorithm can provide high-accuracy parameters of CHM on various decision-making scenarios.