Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall–runoff model calibration

Abstract

An automatic calibration 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: water balance 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 GA with multiple objectives 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. Thirty-four historical floods from 12 years in the Shuangpai Reservoir are applied to calibrate the model parameters whilst 11 floods in recent 2 years are utilized to verify these parameters. Results of this study and application show that the hybrid methodology of GAs and the FOM is not only capable of exploiting more the important characteristics of floods but also efficient and robust.