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Prediction of bed load sediments using different artificial neural network models

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Abstract

Modeling and prediction of bed loads is an important but difficult issue in river engineering. The introduced empirical equations due to restricted applicability even in similar conditions provide different accuracies with each other and measured data. In this paper, three different artificial neural networks (ANNs) including multilayer percepterons, radial based function (RBF), and generalized feed forward neural network using five dominant parameters of bed load transport formulas for the Main Fork Red River in Idaho-USA were developed. The optimum models were found through 102 data sets of flow discharge, flow velocity, water surface slopes, flow depth, and mean grain size. The deficiency of empirical equations for this river by conducted comparison between measured and predicted values was approved where the ANN models presented more consistence and closer estimation to observed data. The coefficient of determination between measured and predicted values for empirical equations varied from 0.10 to 0.21 against the 0.93 to 0.98 in ANN models. The accuracy performance of all models was evaluated and interpreted using different statistical error criteria, analytical graphs and confusion matrixes. Although the ANN models predicted compatible outputs but the RBF with 79% correct classification rate corresponding to 0.191 network error was outperform than others.

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Acknowledgements

The authors greatly expressed their appreciate to Dr. Abbas Abbaszadeh Shahri for his expert advice and encouragement through this study.

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Authors and Affiliations

  1. Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Reza Asheghi & Seyed Abbas Hosseini

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  1. Reza Asheghi
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  2. Seyed Abbas Hosseini
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Correspondence to Seyed Abbas Hosseini.

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Asheghi, R., Hosseini, S.A. Prediction of bed load sediments using different artificial neural network models. Front. Struct. Civ. Eng. 14, 374–386 (2020). https://doi.org/10.1007/s11709-019-0600-0

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