Abstract
Xinanjiang model, a conceptual hydrological model, is well known and widely used in China since 1970s. Xinanjiang model consists of large number of parameters that cannot be directly obtained from measurable quantities of catchment characteristics, but only through model calibration. Parameter optimization is a significant but time-consuming process that is inherent in conceptual hydrological models representing rainfall–runoff processes. This study presents newly developed Particle Swarm Optimization (PSO) and compared with famous Shuffle Complex Evolution (SCE) to auto-calibrate Xinanjiang model parameters. The selected study area is Bedup Basin, located at Samarahan Division, Sarawak, Malaysia. Input data used for model calibration are daily rainfall data Year 2001, and validated with data Year 1990, 1992, 2000, 2002 and 2003. Simulation results are measured with Coefficient of Correlation (R) and Nash-Sutcliffe coefficient (E2). Results show that the performance of PSO is comparable with the famous SCE algorithm. For model calibration, the best R andE2 obtained are 0.775 and 0.715 respectively, compared to R=0.664 and E2=0.677 for SCE. For model validation, the average R=0.859 and average E2=0.892 are obtained for PSO, compared to average R=0.572 and average E2 =0.631 obtained for SCE.Â
Highlights
Xinanjiang model, a semi-distributed conceptual rainfall-runoff model was firstly developed in 1973 and published in English in 1980 (Zhao et al, 1980)
The results show that runoff generated by Xinanjiang model optimized by Particle Swarm Optimization (PSO) algorithm is controlled and dominant to 8 parameters include S, B, Im, Sm, Ex, Ki, Kg and Ci
As the optimal configuration of Xinanjiang model is validated with 5 different years rainfall-runoff data, R values obtained for Year 2000, 2003, 2002, 1992 and 1990 are 0.542, 0.602, 0.583, 0.590 and 0.543 respectively, and 0.717, 0.566, 0,576, 0.646 and 0.650 for E2
Summary
Xinanjiang model, a semi-distributed conceptual rainfall-runoff model was firstly developed in 1973 and published in English in 1980 (Zhao et al, 1980). In this study two Global Optimization methods named as Particle Swarm Optimization (PSO) and Shuffle Complex Evolution (SCE) were identified to calibrate Xinanjiang model parameters automatically. PSO algorithm, a simple group-based stochastic optimization technique, was developed by Kennedy and Eberhart (1995) It is initialized with a group of random particles that were assigned with random positions and velocities. Each particle keeps track of its best fitness position in hyperspace that has achieved so far (Eberhart and Shi, 2001) and accelerate towards its own personal best for every iteration. The fitness value for each particle’s is evaluated by calculating a new velocity term for each particle based on the distance from its personal best, and its distance from the global best position. Stopped the iteration if the number of evolutionary steps has exceeded a predefined value or the criterion value has not improved by a predefined percentage in a predefined number of steps
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