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Design flood estimation in ungauged catchments using genetic algorithm-based artificial neural network (GAANN) technique for Australia

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  • K. Aziz
  • Sohail Rai
  • A. Rahman

Abstract

This paper focuses on the development and testing of the genetic algorithm (GA)-based regional flood frequency analysis (RFFA) models for eastern parts of Australia. The GA-based techniques do not impose a fixed model structure on the data and can better deal with nonlinearity of the input and output relationship. These nonlinear techniques have been applied successfully in many hydrologic problems; however, there have been only limited applications of these techniques in RFFA problems, particularly in Australia. A data set comprising of 452 stations is used to test the GA for artificial neural networks (ANN) optimization known as GAANN. The results from GAANN were compared with the results from back-propagation for ANN optimization known as BPANN. An independent testing shows that both the GAANN and BPANN methods are quite successful in RFFA and can be used as alternative methods to check the validity of the traditional linear models such as quantile regression technique. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • K. Aziz & Sohail Rai & A. Rahman, 2015. "Design flood estimation in ungauged catchments using genetic algorithm-based artificial neural network (GAANN) technique for Australia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(2), pages 805-821, June.
    • Handle: RePEc:spr:nathaz:v:77:y:2015:i:2:p:805-821
    DOI: 10.1007/s11069-015-1625-x
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    References listed on IDEAS

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    1. James Charalambous & Ataur Rahman & Don Carroll, 2013. "Application of Monte Carlo Simulation Technique to Design Flood Estimation: A Case Study for North Johnstone River in Queensland, Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(11), pages 4099-4111, September.
    2. Dragan Savic & Godfrey Walters & James Davidson, 1999. "A Genetic Programming Approach to Rainfall-Runoff Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 13(3), pages 219-231, June.
    3. Elias Ishak & Khaled Haddad & Mohammad Zaman & Ataur Rahman, 2011. "Scaling property of regional floods in New South Wales Australia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 58(3), pages 1155-1167, September.
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    Cited by:

    1. Wei Wang & Jia Liu & Chuanzhe Li & Fuliang Yu & Yuebo Xie & Qingtai Qiu & Yufei Jiao & Guojuan Zhang, 2020. "Assessing the applicability of conceptual hydrological models for design flood estimation in small-scale watersheds of northern China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(3), pages 1135-1153, July.
    2. K. Haddad & A. Rahman, 2020. "Regional flood frequency analysis: evaluation of regions in cluster space using support vector regression," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(1), pages 489-517, May.

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