| dc.creator |
Cimen, Mesut |
|
| dc.date |
2008-05-31T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T11:24:18Z |
|
| dc.date.available |
2020-10-06T11:24:18Z |
|
| dc.identifier |
cff47dc3-b1f2-4ab9-a3c8-49b0cd27b6ed |
|
| dc.identifier |
10.1623/hysj.53.3.656 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/cff47dc3-b1f2-4ab9-a3c8-49b0cd27b6ed/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/72590 |
|
| dc.description |
The use of support vector machines-a new regression procedure in water resources-was investigated for predicting suspended sediment concentration/load in rivers. The method was applied to the observed streamflow and suspended sediment data of two rivers in the USA, which have already been used in earlier studies using soft computing techniques. The estimated suspended sediment values were found to be in good agreement with the observed ones. Negative sediment estimates, which were encountered in the soft computing calculations, are not produced by this method. The results indicate that this approach may give better performance than those described in the literature using different methodologies. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Estimation of daily suspended sediments using support vector machines |
|
| dc.type |
info:eu-repo/semantics/article |
|