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Meta-Heuristic Algorithms for Hydrologic Frequency Analysis

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  • Yousef Hassanzadeh
  • Amin Abdi
  • Siamak Talatahari
  • Vijay Singh

Abstract

Meta-heuristic algorithms, such as the genetic algorithm and ant colony optimization, have received considerable attention in recent years due to their higher ability for solving difficult engineering optimization problems. This paper employs these techniques for estimating parameters of commonly used flood frequency distributions, and compares them with some conventional methods such as maximum likelihood, moments and probability weighted moments using annual maximum discharge data of 14 rivers from East-Azarbaijan, Iran. The results indicate that both the genetic algorithm and ant colony optimization are suitable parameter estimation alternatives. Also, the results of Monte Carlo simulation for various sample sizes, ranging from 20 to 100, demonstrate that the meta-heuristic algorithms yield accurate quantile estimates. Copyright Springer Science+Business Media B.V. 2011

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:25:y:2011:i:7:p:1855-1879
    DOI: 10.1007/s11269-011-9778-1
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    References listed on IDEAS

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    5. 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.
    6. R. Rai & S. Sarkar & V. Singh, 2009. "Evaluation of the Adequacy of Statistical Distribution Functions for Deriving Unit Hydrograph," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(5), pages 899-929, March.
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    Cited by:

    1. Jose-Luis Molina & Raziyeh Farmani & John Bromley, 2011. "Aquifers Management through Evolutionary Bayesian Networks: The Altiplano Case Study (SE Spain)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(14), pages 3883-3909, November.
    2. Qin Tu & Hong Li & Xinkun Wang & Chao Chen, 2015. "Ant Colony Optimization for the Design of Small-Scale Irrigation Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2323-2339, May.
    3. Wei Li & Jianzhong Zhou & Lu Chen & Kuaile Feng & Hairong Zhang & Changqing Meng & Na Sun, 2019. "Upper and Lower Bound Interval Forecasting Methodology Based on Ideal Boundary and Multiple Linear Regression Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1203-1215, February.
    4. Hang Zeng & Ping Feng & Xin Li, 2014. "Reservoir Flood Routing Considering the Non-Stationarity of Flood Series in North China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4273-4287, September.
    5. Shin, Ju-Young & Ouarda, Taha B.M.J. & Lee, Taesam, 2016. "Heterogeneous mixture distributions for modeling wind speed, application to the UAE," Renewable Energy, Elsevier, vol. 91(C), pages 40-52.

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