Suspended sediment modelling using genetic programming and soft computing techniques
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- @Article{Kisi201248,
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author = "Ozgur Kisi and Ali Hosseinzadeh Dailr and
Mesut Cimen and Jalal Shiri",
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title = "Suspended sediment modelling using genetic programming
and soft computing techniques",
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journal = "Journal of Hydrology",
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volume = "450-451",
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pages = "48--58",
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year = "2012",
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ISSN = "0022-1694",
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DOI = "doi:10.1016/j.jhydrol.2012.05.031",
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URL = "http://www.sciencedirect.com/science/article/pii/S0022169412004076",
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keywords = "genetic algorithms, genetic programming, Sediment
load, Artificial intelligence, Estimating, Sensitivity
analysis",
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abstract = "Modelling suspended sediment load is an important
factor in water resources engineering as it crucially
affects the design and management of water resources
structures. In this study the genetic programming (GP)
technique was applied for estimating the daily
suspended sediment load in two stations in Cumberland
River in U.S. Daily flow and sediment data from 1972 to
1989 were used to train and test the applied genetic
programming models. The effect of various GP operators
on sediment load estimation was investigated. The
optimal fitness function, operator functions, linking
function and learning algorithm were obtained for
modelling daily suspended sediment. The GP estimates
were compared with those of the Adaptive Neuro-Fuzzy
Inference System (ANFIS), Artificial Neural Networks
(ANNs) and Support Vector Machine (SVM) results, in
term of coefficient of determination, mean absolute
error, coefficient of residual mass and variance
accounted for. The comparison results indicated that
the GP is superior to the ANFIS, ANN and SVM models in
estimating daily suspended sediment load.",
- }
Genetic Programming entries for
Ozgur Kisi
Ali Hosseinzadeh Dailr
Mesut Cimen
Jalal Shiri
Citations