Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2492
Title: Predicting suspended sediment loads and missing data for Gediz River, Turkey
Authors: Ülke, Aslı
Tayfur, Gökmen
Özkul, Sevinç
Tayfur, Gökmen
Izmir Institute of Technology. Civil Engineering
Keywords: Fuzzy sets
Hydrologic data
Hydrologic models
Regression analysis
Suspended sediment
Flow measurement
Issue Date: 2009
Publisher: American Society of Civil Engineers (ASCE)
Source: Ülke, A., Tayfur, G., and Özkul, S. (2009). Predicting suspended sediment loads and missing data for Gediz River, Turkey. Journal of Hydrologic Engineering, 14(9), 954-965. doi:10.1061/(ASCE)HE.1943-5584.0000060
Abstract: Prediction of suspended sediment load (SSL) is important for water resources quantity and quality studies. The SSL of a stream is generally determined by direct measurement of the suspended sediment concentration or by employing sediment rating curve method. Although direct measurement is the most reliable method, it is very expensive, time consuming, and, in many instances, problematic for inaccessible sections, especially during floods. On the other hand, measuring precipitation and flow discharge is relatively easier and hence, there are more rain and flow gauging stations than SSL gauging stations in Turkey. Furthermore, due to its cost, measurements of SSL are carried out in longer periods compared to precipitation and flow measurements. Although daily precipitation and flow measurements are available for most of the Turkish river basins, at best semimonthly measurements are available for SSL. As such, it is essential to predict SSL from precipitation and flow data and to fill the gap for the missing data records. This study employed artificial intelligence methods of artificial neural networks (ANN) and neurofuzzy inference system, the sediment rating curve method, multilinear regression, and multinonlinear regression methods for this purpose. The comparative analysis of the results showed that the artificial intelligence methods have superiority over the other methods for predicting semimonthly suspended sediment loads. The ANN using conjugate gradient optimization method showed the best performance among the proposed models. It also satisfactorily generated daily SSL data for the missing period record of Gediz River, Turkey.
URI: http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000060
http://hdl.handle.net/11147/2492
ISSN: 1084-0699
0733-9429
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Sürdürülebilir Yeşil Kampüs Koleksiyonu / Sustainable Green Campus Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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