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    Regional Flood Estimation in New South Wales Australia Using Generalized Least Squares Quantile Regression

    Source: Journal of Hydrologic Engineering:;2011:;Volume ( 016 ):;issue: 011
    Author:
    Khaled Haddad
    ,
    Ataur Rahman
    DOI: 10.1061/(ASCE)HE.1943-5584.0000395
    Publisher: American Society of Civil Engineers
    Abstract: This paper investigates the applicability of quantile regression technique (QRT) as a viable regional flood frequency analysis (RFFA) method for the state of New South Wales in Australia. The study uses data from 96 small to medium-sized unregulated basins across New South Wales to develop a generalized least squares (GLS)-based QRT. An independent test employing a wide range of statistical diagnostics indicates that the developed regression equations based on the GLS regression can provide quite accurate flood quantile estimates with median relative error values in the range of 13–42%. The developed regression equations are relatively easy to apply and require data for only three predictors—basin area, design rainfall intensity, and stream density.
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      Regional Flood Estimation in New South Wales Australia Using Generalized Least Squares Quantile Regression

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    contributor authorKhaled Haddad
    contributor authorAtaur Rahman
    date accessioned2017-05-08T21:49:02Z
    date available2017-05-08T21:49:02Z
    date copyrightNovember 2011
    date issued2011
    identifier other%28asce%29he%2E1943-5584%2E0000416.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/63272
    description abstractThis paper investigates the applicability of quantile regression technique (QRT) as a viable regional flood frequency analysis (RFFA) method for the state of New South Wales in Australia. The study uses data from 96 small to medium-sized unregulated basins across New South Wales to develop a generalized least squares (GLS)-based QRT. An independent test employing a wide range of statistical diagnostics indicates that the developed regression equations based on the GLS regression can provide quite accurate flood quantile estimates with median relative error values in the range of 13–42%. The developed regression equations are relatively easy to apply and require data for only three predictors—basin area, design rainfall intensity, and stream density.
    publisherAmerican Society of Civil Engineers
    titleRegional Flood Estimation in New South Wales Australia Using Generalized Least Squares Quantile Regression
    typeJournal Paper
    journal volume16
    journal issue11
    journal titleJournal of Hydrologic Engineering
    identifier doi10.1061/(ASCE)HE.1943-5584.0000395
    treeJournal of Hydrologic Engineering:;2011:;Volume ( 016 ):;issue: 011
    contenttypeFulltext
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