| dc.creator |
KİŞİ, Ozgur |
|
| dc.creator |
DAİLR, Ali Hosseinzadeh |
|
| dc.creator |
ÇİMEN, Mesut |
|
| dc.creator |
Shiri, Jalal |
|
| dc.date |
2012-07-10T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T10:39:56Z |
|
| dc.date.available |
2020-10-06T10:39:56Z |
|
| dc.identifier |
7dcf309e-eabf-490f-aa07-6c97fe5e0391 |
|
| dc.identifier |
10.1016/j.jhydrol.2012.05.031 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/7dcf309e-eabf-490f-aa07-6c97fe5e0391/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/64453 |
|
| dc.description |
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. (c) 2012 Elsevier B.V. All rights reserved. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Suspended sediment modeling using genetic programming and soft computing techniques |
|
| dc.type |
info:eu-repo/semantics/article |
|