Linear genetic programming to scour below submerged pipeline
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- @Article{Azamathulla2011,
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author = "H. Md. Azamathulla and Aytac Guven and
Yusuf Kagan Demir",
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title = "Linear genetic programming to scour below submerged
pipeline",
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journal = "Ocean Engineering",
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volume = "38",
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number = "8-9",
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pages = "995--1000",
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year = "2011",
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month = jun,
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ISSN = "0029-8018",
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DOI = "doi:10.1016/j.oceaneng.2011.03.005",
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URL = "http://www.sciencedirect.com/science/article/B6V4F-52M3TGW-1/2/279184e6554e6b6977d8b9f0180c9f53",
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keywords = "genetic algorithms, genetic programming, Local scour,
Neuro-fuzzy, Pipelines",
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abstract = "Genetic programming (GP) has nowadays attracted the
attention of researchers in the prediction of hydraulic
data. This study presents Linear Genetic Programming
(LGP), which is an extension to GP, as an alternative
tool in the prediction of scour depth below a pipeline.
The data sets of laboratory measurements were collected
from published literature and were used to develop LGP
models. The proposed LGP models were compared with
adaptive neuro-fuzzy inference system (ANFIS) model
results. The predictions of LGP were observed to be in
good agreement with measured data, and quite better
than ANFIS and regression-based equation of scour depth
at submerged pipeline.",
- }
Genetic Programming entries for
Hazi Mohammad Azamathulla
Aytac Guven
Yusuf Kagan Demir
Citations