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  • Evaluation of suspended load transport rate using transport formulas and artificial neural network models (Case study: Chelchay Catchment)

    Author(s)
    Haddadchi, Arman
    Movahedi, Neshat
    Vahidi, Elham
    Omid, Mohammad Hossein
    Dehghani, Amir Ahmad
    Griffith University Author(s)
    Haddadchi, Arman
    Year published
    2013
    Metadata
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    Abstract
    Accurate estimation of sediment load or transport rate is very important to a wide range of water resources projects. This study was undertaken to determine the most appropriate model to predict suspended load in the Chelchay Watershed, northeast of Iran. In total, 59 data series were collected from four gravel bed-rivers and a sand bed river and two depth integrating suspended load samplers to evaluate nine suspended load formulas and feed forward backpropagation Artificial Neural Network (ANN) structures. Although the Chang formula with higher correlation coefficient (r = 0.69) and lower Root Mean Square Error (RMSE = ...
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    Accurate estimation of sediment load or transport rate is very important to a wide range of water resources projects. This study was undertaken to determine the most appropriate model to predict suspended load in the Chelchay Watershed, northeast of Iran. In total, 59 data series were collected from four gravel bed-rivers and a sand bed river and two depth integrating suspended load samplers to evaluate nine suspended load formulas and feed forward backpropagation Artificial Neural Network (ANN) structures. Although the Chang formula with higher correlation coefficient (r = 0.69) and lower Root Mean Square Error (RMSE = 0.013) is the best suspended load predictor among the nine studied formulas, the ANN models significantly outperform traditional suspended load formulas and show their superior performance for all statistical parameters. Among different ANN structures two models including 4 inputs, 4 hidden and one output neurons, and 4 inputs, 4 and one hidden and one output neurons provide the best simulation with the RMSE values of 0.0009 and 0.001, respectively.
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    Journal Title
    Journal of Hydrodynamics
    Volume
    25
    Issue
    3
    DOI
    https://doi.org/10.1016/S1001-6058(11)60385-6
    Subject
    Environmental Engineering Modelling
    Engineering
    Publication URI
    http://hdl.handle.net/10072/56991
    Collection
    • Journal articles

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