Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Huo, Jiuyuana; c; d; * | Liu, Liqunb | Zhang, Yaonanc; d
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: [*] 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:huojy@mail.lzjtu.cn
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.
Keywords: Hydrological model, parameter calibration, multi-objective optimization algorithm, Xinanjiang model, NSGAII algorithm, GDE3 algorithm, SA algorithm
DOI: 10.3233/JCM-160647
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 16, no. 3, pp. 653-669, 2016
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl