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Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels Cover

Design of a new hybrid artificial neural network method based on decision trees for calculating the Froude number in rigid rectangular channels

Open Access
|Jul 2016

Abstract

A vital topic regarding the optimum and economical design of rigid boundary open channels such as sewers and drainage systems is determining the movement of sediment particles. In this study, the incipient motion of sediment is estimated using three datasets from literature, including a wide range of hydraulic parameters. Because existing equations do not consider the effect of sediment bed thickness on incipient motion estimation, this parameter is applied in this study along with the multilayer perceptron (MLP), a hybrid method based on decision trees (DT) (MLP-DT), to estimate incipient motion. According to a comparison with the observed experimental outcome, the proposed method performs well (MARE = 0.048, RMSE = 0.134, SI = 0.06, BIAS = -0.036). The performance of MLP and MLP-DT is compared with that of existing regression-based equations, and significantly higher performance over existing models is observed. Finally, an explicit expression for practical engineering is also provided.

DOI: https://doi.org/10.1515/johh-2016-0031 | Journal eISSN: 1338-4333 | Journal ISSN: 0042-790X
Language: English
Page range: 252 - 260
Submitted on: Oct 16, 2015
Accepted on: May 10, 2016
Published on: Jul 8, 2016
Published by: Slovak Academy of Sciences, Institute of Hydrology; Institute of Hydrodynamics, Czech Academy of Sciences, Prague
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2016 Isa Ebtehaj, Hossein Bonakdari, Amir Hossein Zaji, Charles Hin Joo Bong, Aminuddin Ab Ghani, published by Slovak Academy of Sciences, Institute of Hydrology; Institute of Hydrodynamics, Czech Academy of Sciences, Prague
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.