Suspended sediment modeling using genetic programming and soft computing techniques

► We use genetic programming (GP) technique to model daily suspended sediment load. ► The GP results are compared with those of the other soft computing techniques. ► These techniques are neuro fuzzy, neural network and Support Vector Machines. ► Comparison results show that the GP models perform be...

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Veröffentlicht in:Journal of hydrology (Amsterdam) Jg. 450-451; S. 48 - 58
Hauptverfasser: Kisi, Ozgur, Dailr, Ali Hosseinzadeh, Cimen, Mesut, Shiri, Jalal
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier B.V 11.07.2012
Elsevier
Schlagworte:
algorithms
Artificial intelligence
computer techniques
Earth sciences
Earth, ocean, space
engineering
Estimating
Exact sciences and technology
fuzzy logic
Genetics
Hydrology
Hydrology. Hydrogeology
Marine and continental quaternary
Mathematical analysis
Mathematical models
Neural networks
pollution load
Programming
rivers
Sediment load
Sediments
Sensitivity analysis
Support vector machines
Surficial geology
United States
variance
water management
Water resources
United States
Estimating
Sensitivity analysis
Sediment load
Artificial intelligence
rivers
algorithms
models
sensitivity analysis
neural networks
North America
artificial intelligence
suspended materials
water resources
water resource management
flow
errors
ISSN:0022-1694, 1879-2707
Online-Zugang:Volltext
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Zusammenfassung:► We use genetic programming (GP) technique to model daily suspended sediment load. ► The GP results are compared with those of the other soft computing techniques. ► These techniques are neuro fuzzy, neural network and Support Vector Machines. ► Comparison results show that the GP models perform better than the others. Modeling 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 modeling 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2012.05.031