Using genetic algorithm and TOPSIS for Xinanjing model calibration with a single procedure

Genetic Algorithm (GA) is globally oriented in searching and thus useful in optimizing multiobjective problems, especially where the objective functions are ill-defined. Conceptual rainfall-runoff models that aim at predicting streamflow from the knowledge of precipitation over a catchment have become a basic tool for flood forecasting. The parameter calibration of a conceptual model usually involves the multiple criteria for judging the performances of observed data. However, it is often difficult to derive all objective functions for the parameter calibration problem of a conceptual model. Thus, a new framework to the multiple criteria parameter calibration problem, which combines GA with TOPSIS (technique for order performance by similarity to ideal solution) for Xinanjiang model, is presented. The current method integrates the two parts of Xinanjiang rainfall-runoff model calibration together, simplifying the procedures of model calibration and validation and easily demonstrating the intrinsic phenomenon of observed data in integrity. Comparing results with two-step procedure show that the current methodology is also feasible and robust, but simpler and easier to be applied in practice.