Permeable Breakwaters Performance Modeling: A Comparative Study of Machine Learning Techniques
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- @Article{gandomi:2020:Remote_Sensing,
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author = "Mostafa Gandomi and Moharram {Dolatshahi Pirooz} and
Iman Varjavand and Mohammad Reza Nikoo",
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title = "Permeable Breakwaters Performance Modeling: A
Comparative Study of Machine Learning Techniques",
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journal = "Remote Sensing",
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year = "2020",
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volume = "12",
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number = "11",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2072-4292",
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URL = "https://www.mdpi.com/2072-4292/12/11/1856",
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DOI = "doi:10.3390/rs12111856",
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abstract = "The advantage of permeable breakwaters over more
traditional types has attracted great interest in the
behaviour of these structures in the field of
engineering. The main objective of this study is to
apply 19 well-known machine learning regressors to
derive the best model of innovative breakwater
hydrodynamic behaviour with reflection and transmission
coefficients as the target parameters. A database of
360 laboratory tests on the low-scale breakwater is
used to establish the model. The proposed models link
the reflection and transmission coefficients to seven
dimensionless parameters, including relative chamber
width, relative rockfill height, relative chamber width
in terms of wavelength, wave steepness, wave number
multiplied by water depth, and relative wave height in
terms of rockfill height. For the validation of the
models, the cross-validation method was used for all
models except the multilayer perceptron neural network
(MLP) and genetic programming (GP) models. To validate
the MLP and GP, the database is divided into three
categories: training, validation, and testing.
Furthermore, two explicit functional relationships are
developed by using the GP for each target. The
exponential Gaussian process regression (GPR) model in
predicting the reflection coefficient (R2 = 0.95, OBJ
function = 0.0273), and similarly, the exponential GPR
model in predicting the transmission coefficient (R2 =
0.98, OBJ function = 0.0267) showed the best
performance and the highest correlation with the actual
records and can further be used as a reference for
engineers in practical work. Also, the sensitivity
analysis of the proposed models determined that the
relative height parameter of the rockfill material has
the greatest contribution to the introduced breakwater
behaviour.",
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notes = "also known as \cite{rs12111856}",
- }
Genetic Programming entries for
Mostafa Gandomi
Moharram Dolatshahi Pirooz
Iman Varjavand
Mohammad Reza Nikoo
Citations