Bedload Transport Of Small Rivers In Malaysia

Bedload transport is an essential component of river dynamics and estimation of bedload transport rate is important for practical computations of river morphological variations because the transport of sediment through river channels has major effects on public safety, water resources manageme...

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Main Author:	Sirdari, Zahra Zangeneh
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TA1-2040 Engineering (General) Civil engineering (General)
Online Access:	http://eprints.usm.my/44031/1/Zahra%20Zangeneh%20Sirdari24.pdf
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id	my-usm-ep.44031
record_format	uketd_dc
spelling	my-usm-ep.440312019-04-12T05:26:16Z Bedload Transport Of Small Rivers In Malaysia 2013-05 Sirdari, Zahra Zangeneh TA1-2040 Engineering (General). Civil engineering (General) Bedload transport is an essential component of river dynamics and estimation of bedload transport rate is important for practical computations of river morphological variations because the transport of sediment through river channels has major effects on public safety, water resources management and environmental sustainability. Numerous well-known bedload equations are derived from limited flume experiments or field conditions. These time-consuming equations, based on the relationship between the reliability and representativeness of the data utilized in defining variables and constants, require complex parameters to estimate bedload transport. Thus, a new simple equation based on a balance between simplicity and accuracy is necessary for using in small rivers. In this study the easily accessible data including flow discharge, water depth, slope, and surface grain diameter d50 from the three small rivers in Malaysia used to predict bedload transport. Genetic programming (GP) and artificial neural network (ANN) models that are particularly useful in data interpretation without any restriction to an extensive database are presented as complementary tools for modelling bed load transport in small streams. The ability of GP and ANN as precipitation predictive tools showed to be acceptable. The developed models demonstrate higher performance with an overall accuracy of 97% for ANN and 93% for GP compared with other traditional methods and empirical equations. 2013-05 Thesis http://eprints.usm.my/44031/ http://eprints.usm.my/44031/1/Zahra%20Zangeneh%20Sirdari24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Awam
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TA1-2040 Engineering (General) Civil engineering (General)
spellingShingle	TA1-2040 Engineering (General) Civil engineering (General) Sirdari, Zahra Zangeneh Bedload Transport Of Small Rivers In Malaysia
description	Bedload transport is an essential component of river dynamics and estimation of bedload transport rate is important for practical computations of river morphological variations because the transport of sediment through river channels has major effects on public safety, water resources management and environmental sustainability. Numerous well-known bedload equations are derived from limited flume experiments or field conditions. These time-consuming equations, based on the relationship between the reliability and representativeness of the data utilized in defining variables and constants, require complex parameters to estimate bedload transport. Thus, a new simple equation based on a balance between simplicity and accuracy is necessary for using in small rivers. In this study the easily accessible data including flow discharge, water depth, slope, and surface grain diameter d50 from the three small rivers in Malaysia used to predict bedload transport. Genetic programming (GP) and artificial neural network (ANN) models that are particularly useful in data interpretation without any restriction to an extensive database are presented as complementary tools for modelling bed load transport in small streams. The ability of GP and ANN as precipitation predictive tools showed to be acceptable. The developed models demonstrate higher performance with an overall accuracy of 97% for ANN and 93% for GP compared with other traditional methods and empirical equations.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Sirdari, Zahra Zangeneh
author_facet	Sirdari, Zahra Zangeneh
author_sort	Sirdari, Zahra Zangeneh
title	Bedload Transport Of Small Rivers In Malaysia
title_short	Bedload Transport Of Small Rivers In Malaysia
title_full	Bedload Transport Of Small Rivers In Malaysia
title_fullStr	Bedload Transport Of Small Rivers In Malaysia
title_full_unstemmed	Bedload Transport Of Small Rivers In Malaysia
title_sort	bedload transport of small rivers in malaysia
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Awam
publishDate	2013
url	http://eprints.usm.my/44031/1/Zahra%20Zangeneh%20Sirdari24.pdf
_version_	1747821322597040128

Bedload Transport Of Small Rivers In Malaysia

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