Particle Swarm Optimization for Calibrating and Optimizing Xinanjiang Model Parameters

The Xinanjiang model, a conceptual hydrological model is well known and widely used in China since 1970s. Therefore, most of the parameters in Xinanjiang model have been calibrated and pre-set according to different climate, dryness, wetness, humidity, topography for various catchment areas in China. However, Xinanjiang model is not applied in Malaysia yet and the optimal parameters are not known. The calibration of Xinanjiang model parameters through trial and error method required much time and effort to obtain better results. Therefore, Particle Swarm Optimization (PSO) is adopted to calibrate Xinanjiang model parameters automatically. In this paper, PSO algorithm is used to find the best set of parameters for both daily and hourly models. The selected study area is Bedup Basin, located at Samarahan Division, Sarawak, Malaysia. For daily model, input data used for model calibration was daily rainfall data Year 2001, and validated with data Year 1990, 1992, 2000, 2002 and 2003. A single storm event dated 9th to 12thOctober 2003 was used to calibrate hourly model and validated with 12 different storm events. The accuracy of the simulation results are measured using Coefficient of Correlation (R) and Nash-Sutcliffe Coefficient (E2). Results show that PSO is able to optimize the 12 parameters of Xinanjiang model accurately. For daily model, the best R and E2 for model calibration are found to be 0.775 and 0.715 respectively, and average R=0.622 and E2=0.579 for validation set. Meanwhile, R=0.859 and E2=0.892 are yielded when calibrating hourly model, and the average R and E2 obtained are 0.705 and 0.647 respectively for validation set.

@article{Kuok2012,
  title     = {Particle Swarm Optimization for Calibrating and Optimizing Xinanjiang Model Parameters},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {3},
  number    = {9},
  year      = {2012},
  publisher = {The Science and Information Organization},
  author    = {Kuok King Kuok and Chiu Po Chan},
  doi       = {10.14569/IJACSA.2012.030917},
  url       = {https://doi.org/10.14569/IJACSA.2012.030917}
}