Artificial neural network and regression models for flow velocity at sediment incipient deposition

Mir Jafar Sadegh Safari, Hafzullah Aksoy*, Mirali Mohammadi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)


A set of experiments for the determination of flow characteristics at sediment incipient deposition has been carried out in a trapezoidal cross-section channel. Using experimental data, a regression model is developed for computing velocity of flow in a trapezoidal cross-section channel at the incipient deposition condition and is presented together with already available regression models of rectangular, circular, and U-shape channels. A generalized regression model is also provided by combining the available data of any cross-section. For comparison of the models, a powerful tool, the artificial neural network (ANN) is used for modelling incipient deposition of sediment in rigid boundary channels. Three different ANN techniques, namely, the feed-forward back propagation (FFBP), generalized regression (GR), and radial basis function (RBF), are applied using six input variables; flow discharge, flow depth, channel bed slope, hydraulic radius, relative specific mass of sediment and median size of sediment particles; all taken from laboratory experiments. Hydrodynamic forces acting on sediment particles in the flow are considered in the regression models indirectly for deriving particle Froude number and relative particle size, both being dimensionless. The accuracy of the models is studied by the root mean square error (RMSE), the mean absolute percentage error (MAPE), the discrepancy ratio (Dr) and the concordance coefficient (CC). Evaluation of the models finds ANN models superior and some regression models with an acceptable performance. Therefore, it is concluded that appropriately constructed ANN and regression models can be developed and used for the rigid boundary channel design.

Original languageEnglish
Pages (from-to)1420-1429
Number of pages10
JournalJournal of Hydrology
Publication statusPublished - 1 Oct 2016

Bibliographical note

Publisher Copyright:
© 2016 Elsevier B.V.


Experimental data in this study were taken from the research project no. 113M062, entitled “Incipient deposition of sediment particles in rigid boundary channels”, under coordination of Prof. H. Aksoy, funded by the Scientific and Technological Research Council of Turkey (TUBITAK) . Authors used additional data available in the literature for which they wish to express their sincere gratitude and appreciation to Dr. J.H. Loveless. Continuous, constructive and courageous comments from the reviewers and the editor improved the content and presentation of this paper substantially. These efforts are all greatly appreciated.

FundersFunder number
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu


    • Artificial neural network (ANN)
    • Critical velocity
    • Incipient deposition
    • Open channel
    • Regression model
    • Sediment transport


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