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

Abstract

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 method 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. This study is an immediate further development of authors' previous research (Cheng, C.T., Ou, C.P., Chau, K.W., 2002. Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration. Journal of Hydrology, 268, 72-86), whose obvious disadvantages are to split the whole procedure into two parts and to become difficult to integrally grasp the best behaviors of model during the calibration procedure. The current method integrates the two parts of Xinanjiang rainfall-runoff model calibration together, simplifying the procedures of model calibration and validation and easily demonstrated the intrinsic phenomenon of observed data in integrity. Comparison of results with two-step procedure shows that the current methodology gives similar results to the previous method, is also feasible and robust, but simpler and easier to apply in practice. q

A dual active‐restrictive approach to incorporating environmental flow targets into existing reservoir operation rules

“…The TOPSIS is a popular and widely used approach to solving the MCDM problem [see e.g., AboSinna and Amer, 2005; Cheng et al, 2006;Fu, 2008]. The basic idea behind this approach is that the best option is the least distant one from the positive ideal solution (PIS) and the most distant one from the negative ideal solution (NIS).…”

Calibration of a crop model to irrigated water use using a genetic algorithm

“…Multi-objective GA have been used in the calibration of this model (Bekele and Nicklow, 2007;Whittaker et al, 2007) as well. GA have also been used to calibrate runoff models such as HBV (Seibert, 2000) and TOPSIS (Cheng et al, 2006) as well as crop (Dai et al, 2009) and crop-related models such as SWAP (He et al, 2007).…”

Joint impact of rainfall and tidal level on flood risk in a coastal city with a complex river network: a case study of Fuzhou City, China

“…3). Then, the flow hydrograph of each sub-catchment was calculated through a rainfall run-off model, the Xinanjiang model (Ren-Jun, 1992;Hu et al, 2005;Cheng et al, 2006), which takes the design rainfall processes as its input data. The northern mountain areas were divided into 16 subcatchments from n1 to n16, and the flow hydrograph of each sub-catchment outlet was taken as inflow boundary conditions of the model.…”