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Information Journal Paper

Title

SEDIMENT LOADS PREDICTION USING MULTILAYER FEEDFORWARD NEURAL NETWORKS

Pages

  103-110

Keywords

ARTIFICIAL NEURAL NETWORKS (ANNS) 
MULTI-LAYER PERCEPTRON (MLP) 
RADIAL BASIS FUNCTION (RBF) 

Abstract

SEDIMENT TRANSPORT as a complicated and important phenomenon has attracted a lot of researchers during the last century; however there are some formulae to evaluate sediment loads in aquatic systems. Most of them still face two major problems: firstly, lack of accuracy and secondly, involvement of many parameters which makes them more challenging.Artificial Neural Networks are known as model-free universal function approximators well suited to deal with real life engineering problems including time series predictions and parameter estimation. In this paper, sediment loads are predicted using two different types of multilayer feedforward neural networks, namely Multi-Layer perception (MLP) and Radial Basis Function (RBF). The input variables for both structures are considered to be flow discharge, mean flow depth and width, mean bed material's diameter and water surface slope and the output is sediment discharge. Some different cases have been studied. The results are promising. It has been also observed that mean square prediction errors for the developed MLP is equal to 0.0063 while the devised RBF networks produces much larger mean square errors, namely 0.01260. This indicates that the MLP-load-predictor outperforms the RBF-predictor.

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    APA: Copy

    TAHER SHAMSI, A., MENHAJ, M.B., & AHMADIAN, R.. (2006). SEDIMENT LOADS PREDICTION USING MULTILAYER FEEDFORWARD NEURAL NETWORKS. AMIRKABIR, 16(63-C), 103-110. SID. https://sid.ir/paper/533704/en

    Vancouver: Copy

    TAHER SHAMSI A., MENHAJ M.B., AHMADIAN R.. SEDIMENT LOADS PREDICTION USING MULTILAYER FEEDFORWARD NEURAL NETWORKS. AMIRKABIR[Internet]. 2006;16(63-C):103-110. Available from: https://sid.ir/paper/533704/en

    IEEE: Copy

    A. TAHER SHAMSI, M.B. MENHAJ, and R. AHMADIAN, “SEDIMENT LOADS PREDICTION USING MULTILAYER FEEDFORWARD NEURAL NETWORKS,” AMIRKABIR, vol. 16, no. 63-C, pp. 103–110, 2006, [Online]. Available: https://sid.ir/paper/533704/en

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    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
    مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources
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