Evaluating the apparent shear stress in prismatic compound channels using the Genetic Algorithm based on Multi-Layer Perceptron: A comparative study
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{BONAKDARI:2018:AMC,
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author = "Hossein Bonakdari and Zohreh Sheikh Khozani and
Amir Hossein Zaji and Navid Asadpour",
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title = "Evaluating the apparent shear stress in prismatic
compound channels using the Genetic Algorithm based on
Multi-Layer Perceptron: A comparative study",
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journal = "Applied Mathematics and Computation",
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volume = "338",
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pages = "400--411",
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year = "2018",
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keywords = "genetic algorithms, genetic programming, Apparent
shear stress, Artificial neural network, Compound
channel, Hybrid method",
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ISSN = "0096-3003",
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DOI = "doi:10.1016/j.amc.2018.06.016",
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URL = "http://www.sciencedirect.com/science/article/pii/S0096300318305046",
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abstract = "Apparent shear stress acting on a vertical interface
between the main channel and floodplain in a compound
channel is used to quantify the momentum transfer
between these sub-areas of a cross section. In order to
simulate the apparent shear stress, two soft computing
techniques, including the Genetic Algorithm-Artificial
Neural Network (GA-ANN) and Genetic Programming (GP)
along with Multiple Linear Regression (MLR) were used.
The proposed GA-ANN is a novel self-hidden layer neuron
adjustable hybrid method made by combining the Genetic
Algorithm (GA) with the Multi-Layer Perceptron
Artificial Neural Network (MLP-ANN) method. In order to
find the optimum condition of the methods considered in
modeling apparent shear stress, various input
combinations, fitness functions, transfer functions
(for the GAA method), and mathematical functions (for
the GP method) were investigated. Finally, the results
of the optimum GAA and GP methods were compared with
the MLR as a basic method. The results show that the
hybrid GAA method with RMSE of 0.5326 outperformed the
GP method with RMSE of 0.6651. In addition, the results
indicate that both GAA and GP methods performed
significantly better than MLR with RMSE of 1.5409 in
simulating apparent shear stress in symmetric compound
channels",
- }
Genetic Programming entries for
Hossein Bonakdari
Zohreh Sheikh Khozani
Amir Hossein Zaji
Navid Asadpour
Citations