USING ARTIFICIAL NEURAL NETWORKS (ANNs) FOR SEDIMENT LOAD FORECASTING OF TALKHEROOD RIVER MOUTH

USING ARTIFICIAL NEURAL NETWORKS (ANNs) FOR SEDIMENT LOAD FORECASTING OF TALKHEROOD RIVER MOUTH

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Without a doubt the carried sediment load by a river is the most important factor in creating and formation of the related Delta in the river mouth. Therefore, accurate forecasting of the river sediment load can play a significant role for study on the river Delta. However considering the complexity...

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Main Author: Vahid Nourani
Format: Article
Language:English
Published: University of Paraiba 2009-01-01
Series:Journal of Urban and Environmental Engineering
Online Access:http://www.redalyc.org/articulo.oa?id=283221768001
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spelling doaj-3b77eb2ef7f64f7b8085f20b2329f2822020-11-24T22:03:22ZengUniversity of ParaibaJournal of Urban and Environmental Engineering1982-39322009-01-013116USING ARTIFICIAL NEURAL NETWORKS (ANNs) FOR SEDIMENT LOAD FORECASTING OF TALKHEROOD RIVER MOUTHVahid NouraniWithout a doubt the carried sediment load by a river is the most important factor in creating and formation of the related Delta in the river mouth. Therefore, accurate forecasting of the river sediment load can play a significant role for study on the river Delta. However considering the complexity and non-linearity of the phenomenon, the classic experimental or physical-based approaches usually could not handle the problem so well. In this paper, Artificial Neural Network (ANN) as a non-linear black box interpolator tool is used for modeling suspended sediment load which discharges to the Talkherood river mouth, located in northern west Iran. For this purpose, observed time series of water discharge at current and previous time steps are used as the model input neurons and the model output neuron will be the forecasted sediment load at the current time step. In this way, various schemes of the ANN approach are examined in order to achieve the best network as well as the best architecture of the model. The obtained results are also compared with the results of two other classic methods (i.e., linear regression and rating curve methods) in order to approve the efficiency and ability of the proposed method.http://www.redalyc.org/articulo.oa?id=283221768001
collection DOAJ
language English
format Article
sources DOAJ
author Vahid Nourani
spellingShingle Vahid Nourani
USING ARTIFICIAL NEURAL NETWORKS (ANNs) FOR SEDIMENT LOAD FORECASTING OF TALKHEROOD RIVER MOUTH
Journal of Urban and Environmental Engineering
author_facet Vahid Nourani
author_sort Vahid Nourani
title USING ARTIFICIAL NEURAL NETWORKS (ANNs) FOR SEDIMENT LOAD FORECASTING OF TALKHEROOD RIVER MOUTH
title_short USING ARTIFICIAL NEURAL NETWORKS (ANNs) FOR SEDIMENT LOAD FORECASTING OF TALKHEROOD RIVER MOUTH
title_full USING ARTIFICIAL NEURAL NETWORKS (ANNs) FOR SEDIMENT LOAD FORECASTING OF TALKHEROOD RIVER MOUTH
title_fullStr USING ARTIFICIAL NEURAL NETWORKS (ANNs) FOR SEDIMENT LOAD FORECASTING OF TALKHEROOD RIVER MOUTH
title_full_unstemmed USING ARTIFICIAL NEURAL NETWORKS (ANNs) FOR SEDIMENT LOAD FORECASTING OF TALKHEROOD RIVER MOUTH
title_sort using artificial neural networks (anns) for sediment load forecasting of talkherood river mouth
publisher University of Paraiba
series Journal of Urban and Environmental Engineering
issn 1982-3932
publishDate 2009-01-01
description Without a doubt the carried sediment load by a river is the most important factor in creating and formation of the related Delta in the river mouth. Therefore, accurate forecasting of the river sediment load can play a significant role for study on the river Delta. However considering the complexity and non-linearity of the phenomenon, the classic experimental or physical-based approaches usually could not handle the problem so well. In this paper, Artificial Neural Network (ANN) as a non-linear black box interpolator tool is used for modeling suspended sediment load which discharges to the Talkherood river mouth, located in northern west Iran. For this purpose, observed time series of water discharge at current and previous time steps are used as the model input neurons and the model output neuron will be the forecasted sediment load at the current time step. In this way, various schemes of the ANN approach are examined in order to achieve the best network as well as the best architecture of the model. The obtained results are also compared with the results of two other classic methods (i.e., linear regression and rating curve methods) in order to approve the efficiency and ability of the proposed method.
url http://www.redalyc.org/articulo.oa?id=283221768001
work_keys_str_mv AT vahidnourani usingartificialneuralnetworksannsforsedimentloadforecastingoftalkheroodrivermouth
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