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Genetic Algorithm Based Parameter Estimation of Nash Model

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  • Si-Hui Dong

Abstract

Unit hydrograph method is usually used to resolve surface runoff concentration process. Empirical unit hydrograph is sometimes jagged and not smooth because of estimation errors. Owing to the randomicity of correction method, there is some localization in its application. The application of instantaneous unit hydrograph is relatively wider. Generally, moment method is used to estimate the parameters of instantaneous unit hydrograph. However, the error is obvious between the observed flood process and the predicted flood process with moment method, especially near the flood peak. The genetic algorithm toolbox of matlab software is used to optimize the parameters of instantaneous unit hydrograph. The statistical function gamcdf(x, α, β) in matlab toolbox is used to calculate S(t) curve, which can avoid the errors caused by approximate formula method. The case study indicates that the weighted sum of absolute error applying the method in this paper is 25, the result applying moment method is 63, and the result of approximate formula method is 49. The results show that the method in this paper is more effectual than the other two methods. Copyright Springer Science+Business Media B.V. 2008

Suggested Citation

  • Si-Hui Dong, 2008. "Genetic Algorithm Based Parameter Estimation of Nash Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(4), pages 525-533, April.
    • Handle: RePEc:spr:waterr:v:22:y:2008:i:4:p:525-533
    DOI: 10.1007/s11269-007-9208-6
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    References listed on IDEAS

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    1. Vikrant Jain & R. Sinha, 2003. "Derivation of Unit Hydrograph from GIUH Analysis for a Himalayan River," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 17(5), pages 355-376, October.
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    Cited by:

    1. Samuel Beskow & Lloyd Norton & Carlos Mello, 2013. "Hydrological Prediction in a Tropical Watershed Dominated by Oxisols Using a Distributed Hydrological Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 341-363, January.
    2. Wei Li & Jianzhong Zhou & Huaiwei Sun & Kuaile Feng & Hairong Zhang & Muhammad Tayyab, 2017. "Impact of Distribution Type in Bayes Probability Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 961-977, February.
    3. Chengcheng Huang & Guoqiang Wang & Xiaogu Zheng & Jingshan Yu & Xinyi Xu, 2015. "Simple Linear Modeling Approach for Linking Hydrological Model Parameters to the Physical Features of a River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3265-3289, July.
    4. Yousef Hassanzadeh & Amin Abdi & Siamak Talatahari & Vijay Singh, 2011. "Meta-Heuristic Algorithms for Hydrologic Frequency Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(7), pages 1855-1879, May.

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