Scour depth modelling by a multi-objective evolutionary paradigm
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Laucelli:2011:EMS,
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author = "Daniele Laucelli and Orazio Giustolisi",
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title = "Scour depth modelling by a multi-objective
evolutionary paradigm",
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journal = "Environmental Modelling \& Software",
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year = "2011",
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volume = "26",
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number = "4",
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pages = "498--509",
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keywords = "genetic algorithms, genetic programming, Evolutionary
polynomial regression, Evolutionary computation,
Regression analysis, Multi-objective optimisation,
Local scouring",
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ISSN = "1364-8152",
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URL = "http://www.sciencedirect.com/science/article/pii/S1364815210002859",
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DOI = "doi:10.1016/j.envsoft.2010.10.013",
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size = "12 pages",
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abstract = "Local scour modelling is an important issue in
environmental engineering in order to prevent
degradation of river bed and safeguard the stability of
grade-control structures. Many empirical formulations
can be retrieved from literature to predict the
equilibrium scour depth, which is usually assumed as
representative of the phenomenon. These empirical
equations have been mostly constructed in some ways by
leveraging regression procedures on experimental data,
usually laboratory observations (thus from small/medium
scale experiments). Laboratory data are more accurate
measurements but generally not completely
representative of the actual conditions in real-world
cases, that are often much more complex than those
schematised by the laboratory equipment. This is the
main reason why some of the literature expressions were
not adequate when used for practical applications in
large-scale examples. This work deals with the
application of an evolutionary modelling paradigm,
named Evolutionary Polynomial Regression (EPR), to such
problem. Such a technique was originally presented as a
classical approach, used to achieve a single model for
each analysis, and has been recently updated by
implementing a multi-modelling approach (i.e., to
obtain a set of optimal candidate solutions/models)
where a multi-objective genetic algorithm is used to
get optimal models in terms of parsimony of
mathematical expressions vs. fitting to data. A wide
database of field and laboratory observations is used
for predicting the equilibrium scour depth as a
function of a set of variables characterising the flow,
the sediments and the dimension of the grade-control
structure. Results are discussed considering two
regressive models available in literature that have
been trained on the same data used for EPR. The
proposed modelling paradigm proved to be a useful tool
for data analysis and, in the particular case study,
able to find feasible explicit models featured by an
appreciable generalisation performance.",
- }
Genetic Programming entries for
Daniele B Laucelli
Orazio Giustolisi
Citations