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

Title:

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

Creator:

Ebtehaj, Isa, Bonakdari, Hossein, Zaji, Amir Hossein, Bong, Charles Hin Joo, and Ghani, Aminuddin Ab

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:d7c70fc4-970c-4a82-97a5-4c2e0f352f61
uuid:d7c70fc4-970c-4a82-97a5-4c2e0f352f61
doi:10.1515/johh-2016-0031

Subject:

decision tree, incipient motion, Multilayer perceptron (MLP), and Froude number

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

Slovak

Rights:

http://creativecommons.org/licenses/by-nc-sa/4.0/
policy:public

Coverage:

252-260

Source:

Journal of hydrology and hydromechanics | 2016 Volume:64 | Number:3

Harvested from:

CDK

Metadata only:

false