Genetic Algorithms for Calibrating Water Quality Models

Abstract

The genetic algorithm (GA) is used as an optimization tool to estimate water quality model parameters in a calibration scenario. The GA is found to be a useful calibration tool, capable of providing least-squares parameter estimates while incorporating field observations as constraints and accumulating useful information about the response surface. Because the GA provides a directed, randomized search using a population of points, a database of information about the response surface, parameter correlation, and objective function sensitivity to model parameters is obtained. Synthetic data with and without error are used initially to investigate the potential of the GA for model calibration applications. A case study is then carried out to confirm GA performance with field data. Constraints are included successfully in the GA search using either a penalty function or a special decoding operation. However, 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.