Application of artificial neural network and genetic programming models for estimating the longitudinal velocity field in open channel junctions
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{Zaji:2015:FMI,
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author = "Amir Hossein Zaji and Hossein Bonakdari",
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title = "Application of artificial neural network and genetic
programming models for estimating the longitudinal
velocity field in open channel junctions",
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journal = "Flow Measurement and Instrumentation",
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volume = "41",
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pages = "81--89",
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year = "2015",
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ISSN = "0955-5986",
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DOI = "doi:10.1016/j.flowmeasinst.2014.10.011",
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URL = "http://www.sciencedirect.com/science/article/pii/S0955598614001307",
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abstract = "Estimating the accurate longitudinal velocity fields
in an open channel junction has a great impact on
hydraulic structures such as irrigation and drainage
channels, river systems and sewer networks. In this
study, Genetic Programming (GP) and Multi-Layer
Perceptron Artificial Neural Network (MLP-ANN) were
modelled and compared to find an analytical formulation
that could present a continuous spatial description of
velocity in open channel junction by using discrete
information of laboratory measurements. Three direction
coordinates of each point of the fluid flow and
discharge ratio of main to tributary channel were used
as inputs to the GP and ANN models. The training and
testing of the models were performed according to the
published experimental data from the related
literature. To find the accurate prediction ability of
GP and ANN models in cases with minor training dataset,
the models were compared with various percents of
allocated data to train dataset. New formulations were
obtained from GP and ANN models that can be applied for
practical longitudinal velocity field prediction in an
open channel junction. The results showed that ANN
model by Root Mean Squared Error (RMSE) of 0.068
performs better than GP model by RMSE of 0.162, and
that ANN can model the longitudinal velocity field with
small population of train dataset with high accuracy.",
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keywords = "genetic algorithms, genetic programming, Open channel
junction, Artificial neural network, Longitudinal
velocity fields",
- }
Genetic Programming entries for
Amir Hossein Zaji
Hossein Bonakdari
Citations