| Yazarlar (4) |
Kasım Yenigün
Harran Üniversitesi, Türkiye |
Prof. Dr. Mahmut BİLGEHAN
İstanbul Arel Üniversitesi, Türkiye |
|
Reşit Gerger
Harran Üniversitesi, Türkiye |
|
Mehmet Mutlu
|
| Özet |
| Agricultural fields’ fertility decays and dam reservoirs are filled due to sediment movement. Sediment amount which is carried by a river depends on the river’s flow rate, inclination of its base and time. In this study, sediment estimations of Euphrates basin which was selected as the area for practice, is the largest basin in Turkey and contains its largest dams, based on classical formulations like Du Boys, Meyer-Peter-Müller, Schoklitsch, Shields and Garde-Albertson. Then, sediment values were estimated by using artificial neural networks (ANN) having a network architecture, which was developed by the authors. High correlation was observed between the values found by using a feed-forward and backpropagation and the observed values of ANN. This evidence, emphasizes how effective and efficient this method is, compared with classical methods. Design of reservoirs dead storages depends on realistic and reliable estimation of sediment yield. In this study, more realistic values have been obtained with ANN model compared with classical equations. Moreover, when sediment measurement cannot be conducted for a certain period, its amounts for the absent period may be estimated by using ANN technique with a little error. |
| Anahtar Kelimeler |
| Sediment yield,back propogation,artificial neural network,Euphrates basin |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | International Journal of Physical Sciences |
| Dergi ISSN | 1992-1950 |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Makale Dili | İngilizce |
| Basım Tarihi | 05-2010 |
| Cilt No | 5 |
| Sayı | 5 |
| Sayfalar | 518 / 534 |
