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  • Application of neural networks and fuzzy logic models to long-shore sediment transport

    Author(s)
    Kabiri-Samani, AR
    Aghaee-Tarazjani, J
    Borghei, SM
    Jeng, DS
    Griffith University Author(s)
    Jeng, Dong-Sheng
    Year published
    2011
    Metadata
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    Abstract
    Predictions of long-shore sediment transport rate (LSTR) are a vital task for coastal engineers in the determination of erosion or accretion along coasts. Many scientists have tried to find empirical method for the estimation of LSTR in the past decades. However, due to the influence of significant number of parameters and randomness of the data, the existing empirical methods provide quite different results and have limited applications. In this paper, an alternative approach, fuzzy logic and neural network, is proposed to estimate LSTR. Six dominant variables on LSTR are considered in the present models, including wave ...
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    Predictions of long-shore sediment transport rate (LSTR) are a vital task for coastal engineers in the determination of erosion or accretion along coasts. Many scientists have tried to find empirical method for the estimation of LSTR in the past decades. However, due to the influence of significant number of parameters and randomness of the data, the existing empirical methods provide quite different results and have limited applications. In this paper, an alternative approach, fuzzy logic and neural network, is proposed to estimate LSTR. Six dominant variables on LSTR are considered in the present models, including wave breaking height (Hbs), wave period (T), wave breaking angle (?bs), beach slope (m), grain size (D) and sediment mass flow rate along shore (Qs). A comprehensive comparison between both neural networks and fuzzy logic models and the existing empirical formulae will be presented to demonstrate capacity of fuzzy logic and artificial neural network.
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    Journal Title
    Applied Soft Computing
    Volume
    11
    Issue
    2
    DOI
    https://doi.org/10.1016/j.asoc.2010.11.021
    Subject
    Civil Geotechnical Engineering
    Applied Mathematics
    Artificial Intelligence and Image Processing
    Information Systems
    Publication URI
    http://hdl.handle.net/10072/63837
    Collection
    • Journal articles

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