Prediction of sediment load by sediment rating curve and neural network (ANN) in El Kebir catchment, Algeria
نویسندگان
چکیده
منابع مشابه
Estimating Suspended Sediment by Artificial Neural Network (ANN), Decision Trees (DT) and Sediment Rating Curve (SRC) Models (Case study: Lorestan Province, Iran)
The aim of this study was to estimate suspended sediment by the ANN model, DT with CART algorithm and different types of SRC, in ten stations from the Lorestan Province of Iran. The results showed that the accuracy of ANN with Levenberg-Marquardt back propagation algorithm is more than the two other models, especially in high discharges. Comparison of different intervals in models showed that r...
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Sediment rating curve (SRC) is a conventional and a common regression model in estimating suspended sediment load (SSL) of flow discharge. However, in most cases the data log-transformation in SRC models causing a bias which underestimates SSL prediction. In this study, using the daily stream flow and suspended sediment load data from Shalman hydrometric station on Shalmanroud River, Guilan Pro...
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Estimation of sediment loads in rivers is one of the most important, difficult components of sediment transport studies and river engineering. Accessing new methods that can be effective in this background are more important. In this research, we have used the artificial neural network (ANN) to optimize the results of the sediment rating curve (SRC) to predict the suspended sediment loads. For ...
متن کاملestimation suspended sediment load with sediment rating curve and artificial neural network method (case study: lorestan province)
suspended sediment estimation is an important factor from different aspects including, farming, soil conservation, dams, aquatic life, as well as various aspects of the research. there are different methods for suspended sediment estimation. this study aims to estimate suspended sediment using feed forward neural network with error back propagation with levenberg-marquardt back propagation algo...
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ژورنال
عنوان ژورنال: Journal of Earth System Science
سال: 2013
ISSN: 0253-4126,0973-774X
DOI: 10.1007/s12040-013-0347-2