Artificial neural network sediment transport model for Sungai Bernam / Muhammad Syahreen Sa'adon

Artificial neural network (ANN) model is proposed as an alternative to the conventional sediment transport models for Sungai Bernam. This study evaluates the existing sediment transport equations against the local river data. Equations used in evaluations are Ackers & White, Ariffin, Engelund...

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Main Author:	Sa'adon, Muhammad Syahreen
Format:	Thesis
Language:	English
Published:	2008
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/564/1/TM_MUHAMMAD%20SYAHREEN%20SA%27ADON%20EC%2008_5%201.pdf
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spelling	my-uitm-ir.5642016-07-01T03:52:50Z Artificial neural network sediment transport model for Sungai Bernam / Muhammad Syahreen Sa'adon 2008 Sa'adon, Muhammad Syahreen Neural networks (Computer science) TA Engineering. Civil engineering Artificial neural network (ANN) model is proposed as an alternative to the conventional sediment transport models for Sungai Bernam. This study evaluates the existing sediment transport equations against the local river data. Equations used in evaluations are Ackers & White, Ariffin, Engelund & Hansen, Einstein-Brown, Graf and Yang equations. Selections of the equations are based on their performance by previous investigator. Accuracy of the proposed sediment model was evaluated using the discrepancy ratio. Discrepancy ratio is the ratio of predicted to measured sediment values. From the evaluations, Engelund and Hansen equation gave the best prediction when tested against the local river data. In this study, an improvement on the equation was made and ANN is used as a tool in analysis. The proposed architecture of the sediment model is a 3 layer multi perceptron model (2:3:3:3:1) with two neurons in the input layer. The hidden layer consisting of three slabs and each slab in the hidden layer has 3 neurons. The output neuron is the total sediment load. The momentum rate parameter and learning rate parameter are 0.4, and 0.3 respectively. The proposed model was trained using 36 sets of field data and was further validated using a different set of field data. In both training and testing phases, the proposed models yield about 90% accuracy. The accuracy of the model was measured using the discrepancy ratio. Range of discrepancy ratio used for measurement of accuracy is 0.5 to 2.0. 2008 Thesis https://ir.uitm.edu.my/id/eprint/564/ https://ir.uitm.edu.my/id/eprint/564/1/TM_MUHAMMAD%20SYAHREEN%20SA%27ADON%20EC%2008_5%201.pdf text en public masters Universiti Teknologi MARA Faculty of Civil Engineering
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Neural networks (Computer science) Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Neural networks (Computer science) Sa'adon, Muhammad Syahreen Artificial neural network sediment transport model for Sungai Bernam / Muhammad Syahreen Sa'adon
description	Artificial neural network (ANN) model is proposed as an alternative to the conventional sediment transport models for Sungai Bernam. This study evaluates the existing sediment transport equations against the local river data. Equations used in evaluations are Ackers & White, Ariffin, Engelund & Hansen, Einstein-Brown, Graf and Yang equations. Selections of the equations are based on their performance by previous investigator. Accuracy of the proposed sediment model was evaluated using the discrepancy ratio. Discrepancy ratio is the ratio of predicted to measured sediment values. From the evaluations, Engelund and Hansen equation gave the best prediction when tested against the local river data. In this study, an improvement on the equation was made and ANN is used as a tool in analysis. The proposed architecture of the sediment model is a 3 layer multi perceptron model (2:3:3:3:1) with two neurons in the input layer. The hidden layer consisting of three slabs and each slab in the hidden layer has 3 neurons. The output neuron is the total sediment load. The momentum rate parameter and learning rate parameter are 0.4, and 0.3 respectively. The proposed model was trained using 36 sets of field data and was further validated using a different set of field data. In both training and testing phases, the proposed models yield about 90% accuracy. The accuracy of the model was measured using the discrepancy ratio. Range of discrepancy ratio used for measurement of accuracy is 0.5 to 2.0.
format	Thesis
qualification_level	Master's degree
author	Sa'adon, Muhammad Syahreen
author_facet	Sa'adon, Muhammad Syahreen
author_sort	Sa'adon, Muhammad Syahreen
title	Artificial neural network sediment transport model for Sungai Bernam / Muhammad Syahreen Sa'adon
title_short	Artificial neural network sediment transport model for Sungai Bernam / Muhammad Syahreen Sa'adon
title_full	Artificial neural network sediment transport model for Sungai Bernam / Muhammad Syahreen Sa'adon
title_fullStr	Artificial neural network sediment transport model for Sungai Bernam / Muhammad Syahreen Sa'adon
title_full_unstemmed	Artificial neural network sediment transport model for Sungai Bernam / Muhammad Syahreen Sa'adon
title_sort	artificial neural network sediment transport model for sungai bernam / muhammad syahreen sa'adon
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Civil Engineering
publishDate	2008
url	https://ir.uitm.edu.my/id/eprint/564/1/TM_MUHAMMAD%20SYAHREEN%20SA%27ADON%20EC%2008_5%201.pdf
_version_	1783732939969789952

Artificial neural network sediment transport model for Sungai Bernam / Muhammad Syahreen Sa'adon

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