Invasive weed optimization algorithm-based adaptive neuro-fuzzy inference system hybrid model for sediment transport with a bed deposit
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- @Article{SADEGHSAFARI:2020:cleaner,
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author = "Mir Jafar {Sadegh Safari} and Babak Mohammadi and
Katayoun Kargar",
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title = "Invasive weed optimization algorithm-based adaptive
neuro-fuzzy inference system hybrid model for sediment
transport with a bed deposit",
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journal = "Journal of Cleaner Production",
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pages = "124267",
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year = "2020",
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ISSN = "0959-6526",
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DOI = "doi:10.1016/j.jclepro.2020.124267",
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URL = "http://www.sciencedirect.com/science/article/pii/S0959652620343122",
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keywords = "genetic algorithms, genetic programming, adaptive
neuro-fuzzy inference system, deposited bed width,
invasive weed optimization, open channel, sediment
transport",
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abstract = "Inasmuch as channels are designed to mitigate
continues sedimentation, sediment transport models have
been developed to calculate flow velocity to keep
sediment particles in motion. In order to promote the
computation capability of sediment transport models,
recently machine learning algorithms have attracted
interests, extensively. However, accuracy of such a
model is attributed to the range of data and applied
technique for model construction. For this purpose, the
current study scrutinizes the applicability of
{"}non-deposition with deposited bed{"} (NDB) concept
for design of large channels applying hybrid machine
learning algorithms. Through the modeling, firstly,
conventional adaptive neuro-fuzzy inference system
(ANFIS) technique is applied to develop a stand-alone
model. In furtherance of improving the model's
performance, the ANFIS is hybridized with invasive weed
optimization (IWO) algorithm to construct the hybrid
ANFIS-IWO model. As a benchmark, the ANFIS is further
hybridized with classical genetic algorithm (GA) to
compare with ANFIS-IWO outcomes. Furthermore, the
developed machine learning models are compared to
multigene genetic programming (MGP) and particle swarm
optimization (PSO) stand-alone machine learning results
reported in the literature and classical regression
models by means of variety of statistical performance
measurements. Hybridization of ANFIS with IWO, enhances
its accuracy with a factor of 30percent. Respecting to
the models performance examination, the ANFIS-IWO model
is found superior to its alternatives for sediment
transport computation. The thickness of the deposited
bed and deposited bed width are found as effective
parameters for sediment transport modeling in open
channels with a bed deposit",
- }
Genetic Programming entries for
Mir Jafar Sadegh Safari
Babak Mohammadi
Katayoun Kargar
Citations