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Application of artificial neural networks in regional flood frequency analysis: a case study for Australia

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Abstract

Regional flood frequency analysis (RFFA) is widely used in practice to estimate flood quantiles in ungauged catchments. Most commonly adopted RFFA methods such as quantile regression technique (QRT) assume a log-linear relationship between the dependent and a set of predictor variables. As non-linear models and universal approximators, artificial neural networks (ANN) have been widely adopted in rainfall runoff modeling and hydrologic forecasting, but there have been relatively few studies involving the application of ANN to RFFA for estimating flood quantiles in ungauged catchments. This paper thus focuses on the development and testing of an ANN-based RFFA model using an extensive Australian database consisting of 452 gauged catchments. Based on an independent testing, it has been found that ANN-based RFFA model with only two predictor variables can provide flood quantile estimates that are more accurate than the traditional QRT. Seven different regions have been compared with the ANN-based RFFA model and it has been shown that when the data from all the eastern Australian states are combined together to form a single region, the ANN presents the best performing RFFA model. This indicates that a relatively larger dataset is better suited for successful training and testing of the ANN-based RFFA models.

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Acknowledgments

The authors would like to acknowledge the financial supports of Geoscience Australia and Engineers Australia and various government and private organizations in Australia that provided the data for the project: Department of Sustainability and Environment (VIC), Australian Bureau of Meteorology, Department of Natural Resources and Water (QLD), Department of Water and Energy (NSW) and ENTURA (TAS). The authors would also like to thank two anonymous reviewers and the Associate Editor for very useful comments which have helped to improve the paper notably.

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

    K. Aziz, A. Rahman, G. Fang & S. Shrestha

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  1. K. Aziz
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Correspondence to A. Rahman.

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Aziz, K., Rahman, A., Fang, G. et al. Application of artificial neural networks in regional flood frequency analysis: a case study for Australia. Stoch Environ Res Risk Assess 28, 541–554 (2014). https://doi.org/10.1007/s00477-013-0771-5

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