The Genetic Algorithm (GA) is often associated with local search optimisation techniques in the calibration process of Conceptual Rainfall-Runoff Models (CRRMs) (Wang, 1991; Franchini, 1996), i.e. the GA is used for approaching the region encompassing the global solution and then its results are used as a starting point for the local optimizer in the subsequent “fine-tuning” process. However, the GA can be formulated in very many ways. This study analyses various GA structures and their robustness and efficiency. In addition, a sensitivity analysis of the various schemes to their own parameters is performed. The analysis is conducted using an 11-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 data 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, showing that, in the calibration of the ADM, the PS may give a slightly superior performance. © 1997 Taylor & Francis Group, LLC.
Comparing several genetic algorithm schemes for the calibration of conceptual rainfall-runoff models
FRANCHINI, Marco;
1997
Abstract
The Genetic Algorithm (GA) is often associated with local search optimisation techniques in the calibration process of Conceptual Rainfall-Runoff Models (CRRMs) (Wang, 1991; Franchini, 1996), i.e. the GA is used for approaching the region encompassing the global solution and then its results are used as a starting point for the local optimizer in the subsequent “fine-tuning” process. However, the GA can be formulated in very many ways. This study analyses various GA structures and their robustness and efficiency. In addition, a sensitivity analysis of the various schemes to their own parameters is performed. The analysis is conducted using an 11-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 data 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, showing that, in the calibration of the ADM, the PS may give a slightly superior performance. © 1997 Taylor & Francis Group, LLC.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
