COMPARING SEVERAL GENETIC ALGORITHM SCHEMES FOR THE CALIBRATION OF CONCEPTUAL RAINFALL-RUNOFF MODELS

The Genetic Algorithm (GA) is often associated with local search optim isation techniques in the calibration process of Conceptual Rainfall-R unoff Models (CRRMs) (Wang, 1991; Franchini, 1996), i.e. the GA is use d for approaching the region encompassing the global solution and then its results are used as a starting point for the local optimizer in t he subsequent ''fine-tuning'' process. However, the GA can be formulat ed in very many ways. This study analyses various GA structures and th eir robustness and efficiency. In addition, a sensitivity analysis of the various schemes to their own parameters is performed. The analysis is conducted using an Ii-parameter CRRM, called A Distributed Model ( ADM), applied to both a theoretical case without model and data errors and two cases of the real world in which there are both model and dat a errors. Finally, assuming the same role as the GA for the ''Pattern Search'' (PS) method in a two-step optimisation technique (Hendrickson et al., 1988), the results of the two algorithms are compared, showin g that, in the calibration of the ADM, the PS may give a slightly supe rior performance.