Combining a fuzzy optimal model with a genetic algorithm for

An automatic calibration framework and methodology for the Xinanjiang model that has been successfully and widely applied in China is presented. The automatic calibration of the model consists of two parts: runoff parameter and runoff routing parameter calibration. The former is based on a simple genetic algorithm (GA). The latter is based on a new method which combines a fuzzy optimal model (FOM) with a GA for solving the multiple objective runoff routing parameters calibration problem. Except for the specific fitness where the membership degree of alternative obtained by FOM with limited alternatives and multi-objectives is employed, the multiple objective GA developed in this paper is otherwise the same as the simple GA. The parameter calibration includes optimization of multiple objectives: (1) peak discharge, (2) peak time and (3) total runoff volume. 34 historical floods from 12 years in the Shuangpai Reservoir are applied to calibrate the model parameters whilst 11 floods in recent two years are utilized to verify these parameters. Results of this study and application show that GAs not only can improve forecast accuracy but are also efficient and robust means.