estimating suspended sediment by artificial neural network (ann), decision trees (dt) and sediment rating curve (src) models (case study: lorestan province, iran)
نویسندگان
چکیده
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 running models with monthly data,resulted in smaller error and better estimated results. moreover, results showed that using minimum variance unbiased estimator (mvue) bias correction factor modified the src results, especially in monthly time steps in almost all stations. hence, it can be said that if because of advantages such as simplicity, src models are preferred, it is better that msrc (modified sediment rating curve) is used in monthly period.
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...
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...
متن کاملInvestigation 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 ...
متن کاملcomparison of daily suspended sediment load estimations by sediment rating curve and neural network models (case study: ghazaghli station in golestan province)
0
متن کاملApplying 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...
متن کاملOptimization 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...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
civil engineering infrastructures journalجلد ۴۸، شماره ۲، صفحات ۳۷۳-۳۸۰
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023