Automatic Calibration of a Surface Water Quality Model using a Hybrid Genetic Algorithm

Water quality models are helpful for rationalizing water quality management. Their success depends largely on how well they are calibrated. The automatic calibration of models usually outperforms traditional trial-and-error process. In this study, a real- coded genetic algorithm (GA) was combined with the Nelder-Mead simplex (NMS) algorithm to form a hybrid approach, GA-NMS. It was employed to calibrate simulated vertical profiles of temperature and concentration of chlorophyll a by CE-QUAL-W2 to measured values in Lake Maumelle, USA. A set of parameter values that had the lowest objective function value was obtained in hundreds of objective function evaluations. It produces reasonable agreement between measurements and simulations. This application demonstrated that the approach can be used in the automatic calibration of water quality models.