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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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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.

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Acknowledgments

The authors would like to acknowledge Engineers Australia to provide funding for this task, various state water agencies, and Australian Bureau of Meteorology for providing data for the study. The data used in this study have been prepared as part of Australian Rainfall and Runoff (ARR) Revision Project 5 Regional flood methods. The authors would like to acknowledge Dr Gu Fang, A/Prof Surendra Shrestha, Professor George Kuczera, Mr Erwin Weinmann, Mr Mark Babister, A/Prof James Ball, Dr Khaled Haddad, and Dr William Weeks for their comments and suggestion on the project.

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  1. School of Computing, Engineering and Mathematics, University of Western Sydney, Sydney, NSW, Australia

    K. Aziz & A. Rahman

  2. Murray-Darling Basin Authority, Campbelltown, ACT, Australia

    Sohail Rai

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  1. K. Aziz
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  2. Sohail Rai
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  3. A. Rahman
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Correspondence to A. Rahman.

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Aziz, K., Rai, S. & Rahman, A. Design flood estimation in ungauged catchments using genetic algorithm-based artificial neural network (GAANN) technique for Australia. Nat Hazards 77, 805–821 (2015). https://doi.org/10.1007/s11069-015-1625-x

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