GENETIC ALGORITHMS FOR CALIBRATING WATER-QUALITY MODELS

The genetic algorithm (GA) is used as an optimization tool to estimate water quality model parameters in a calibration scenario. The GA is f ound to be a useful calibration tool, capable of providing least-squar es parameter estimates while incorporating field observations as const raints and accumulating useful information about the response surface. Because the GA provides a directed, randomized search using a populat ion of points, a database of information about the response surface, p arameter correlation, and objective function sensitivity to model para meters is obtained. Synthetic data with and without error are used ini tially to investigate the potential of the GA for model calibration ap plications. A case study is then carried out to confirm GA performance with field data. Constraints are included successfully in the GA sear ch using either a penalty function or a special decoding operation. Ho wever, results show that the GA with the penalty function outperforms the GA with the decoder. Furthermore, parameter estimation is found to be improved by the inclusion of multiple-response data. For ill-posed problems, the GA provides several parameter estimates, all performing equally well mathematically.