Predicting Suspended Sediment Loads and Missing Data for Gediz River, Turkey
Open Access
- 1 September 2009
- journal article
- Published by American Society of Civil Engineers (ASCE) in Journal of Hydrologic Engineering
- Vol. 14 (9) , 954-965
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...Keywords
This publication has 26 references indexed in Scilit:
- A genetic programming approach to suspended sediment modellingJournal of Hydrology, 2008
- Estimation of total sediment load concentration obtained by experimental study using artificial neural networksEnvironmental Fluid Mechanics, 2007
- The accuracy of sediment loads when log-transformation produces nonlinear sediment load–discharge relationshipsJournal of Hydrology, 2007
- Suspended sediment load simulation by two artificial neural network methods using hydrometeorological dataEnvironmental Modelling & Software, 2007
- Flow prediction by three back propagation techniques using k-fold partitioning of neural network training dataHydrology Research, 2005
- Estimation and forecasting of daily suspended sediment data by multi-layer perceptronsAdvances in Water Resources, 2004
- Feed forward neural networks modeling for K–P interactionsChaos, Solitons & Fractals, 2003
- Fitting and interpretation of sediment rating curvesJournal of Hydrology, 2000
- Artificial Neural Networks in Hydrology. I: Preliminary ConceptsJournal of Hydrologic Engineering, 2000
- Approximation by superpositions of a sigmoidal functionMathematics of Control, Signals, and Systems, 1989