Comparative Study of Evolutionary Algorithms for the Automatic Calibration of the Medbasin-D Conceptual Hydrological Model

Abstract

The calibration of a hydrological model is an important task for obtaining accurate runoff simulation results for a specific watershed. Several optimisation algorithms have been applied during the last years for the automatic calibration of conceptual rainfall-runoff (CRR) models. The aim of this study is to compare the effectiveness and the efficiency of three evolutionary algorithms, namely the Shuffled Complex Evolution (SCE), the Genetic Algorithms (GA) and the Evolutionary Annealing-Simplex (EAS), for the calibration of the Medbasin-D daily CRR model. An improved calibration approach of Medbasin-D is presented, including a batch-processing module which enables the implementation of coupled simulation-optimisation routines. The enhanced Medbasin calibration module is employed in a watershed of the island of Crete (Greece), using several objective functions in order to test the optimisation algorithms under different hydrological flow conditions. The results reveal that, in terms of effectiveness, SCE and EAS performed equally well, while GA provided slightly worse optimal solutions. However, GA was computationally more efficient than SCE and EAS. Despite the discrepancies among the optimisation runs, the simulated hydrographs had a very similar response for the optimal parameter sets obtained by the same calibration criteria, indicating that all tested optimisation methods produce equally successful results with Medbasin-D model. Additionally, the selected objective function seems to have a more decisive effect on the final simulation outcomes.