estimation suspended sediment load with sediment rating curve and artificial neural network method (case study: lorestan province)

Authors

محسن یوسفی

کارشناس ارشد آبخیزداری، دانشکدة منابع طبیعی و کویرشناسی، دانشگاه یزد، ایران فاطمه برزگر

عضو هیئت علمی دانشکدة کشاورزی دانشگاه پیام نور، ایران

abstract

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 algorithm and compare the results with best sediment rating curves among commonly used sediment rating curves, including: linear, seasonal, monthly and mean load within discharge classes. to attain this, the sediment discharge and the corresponding water discharge data for ten hydrometric stations of lorestan province of iran were used. in next step different methods of sediment rating curves along with different correction factors, a total of 20 methods were applied to data. results showed among examined methods; monthly rating curve with muve correction factor has been selected as best, based on nash and sutcliffe index and accuracy index. then results of estimating sediment load by using selected sediment rating curve were compared with the results of the neural network. mean-square error and nash and sutcliffe index were applied to select more appropriate method. the results showed the suitability of the feed forward neural network error propagation in compare with sediment rating curves.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

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...

full text

Investigation of Possibility of Suspended Sediment Prediction Using a Combination of Sediment Rating Curve and Artificial Neural Network Case Study: Ghatorchai River, Yazdakan Bridge

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 ...

full text

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...

full text

Applying Artificial Neural Network Algorithms to Estimate Suspended Sediment Load (Case Study: Kasilian Catchment, Iran)

Estimate of sediment load is required in a wide spectrum of water resources engineering problems. The nonlinear nature of suspended sediment load series necessitates the utilization of nonlinear methods to simulate the suspended sediment load. In this study Artificial Neural Networks (ANNs) are employed to estimate daily suspended sediment load. Two different ANN algorithms, Multi Layer Perce...

full text

Optimization of sediment rating curve coefficients using evolutionary algorithms and unsupervised artificial neural network

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...

full text

My Resources

Save resource for easier access later


Journal title:
مرتع و آبخیزداری

جلد ۶۸، شماره ۲، صفحات ۴۱۳-۴۲۶

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023