Affiliations: [a] School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu, China | [b] College of Information Science and Technology, Gansu Agricultural University, Lanzhou, Gansu, China | [c] Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu, China | [d] Gansu Data Engineering and Technology Research Center for Resources and Environment, Lanzhou, Gansu, China
Correspondence:
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Corresponding author: Jiuyuan Huo, School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China. Tel.: +86 0931 4956251; Fax: +86 0931 4956251; E-mail:[email protected]
Abstract: Parameter optimization and calibration of the
hydrological model has been one of the important research fields in
hydrological forecasting. This paper is written to address the inherent
defects that traditional parameter optimization of Xinanjiang hydrological
model with a single objective entails. These methods cannot fully exploit
hydrological characteristics information from hydrological observation. We
selected the Nash Sutcliffe coefficient, which is known to be biased for
high flows and the logarithmic form of the Nash Sutcliffe coefficient that
emphasize low-flow values as the objective functions. Then, we adopted the
multi-objective optimization algorithms, such as the Nondominated Sorted
Genetic Algorithm-II (NSGAII) and the Third Evolution Step of Generalized
Differential Evolution (GDE3), and the single-objective optimization
algorithm, Simulated Annealing (SA). These algorithms were applied in Heihe
River Basin to calibrate parameters of the Xinanjiang hydrological model for
long-term prediction of river discharges. Through the evaluation of the
Pareto optimal parameter set derived from multi-objective optimization
algorithms and the optimal solution obtained from the single objective
algorithm, the results showed that the multi-objective optimization
algorithms, in particular the NSGA-II algorithm, perform best to locate the
Pareto optimal solutions in the parameter search space. They can also obtain
better results with respect to the model parameters calibrated by the single
objective algorithm. The major contribution of this work is the comparative
application research of single-objective optimization with the
multi-objective optimization algorithms for the parameters optimization of
the Xinanjiang model in the Heihe River basin.
