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...
| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.usm.my/44031/ http://eprints.usm.my/44031/1/Zahra%20Zangeneh%20Sirdari24.pdf |
| _version_ | 1848879957944565760 |
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| author | Sirdari, Zahra Zangeneh |
| author_facet | Sirdari, Zahra Zangeneh |
| author_sort | Sirdari, Zahra Zangeneh |
| building | USM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T17:55:29Z |
| format | Thesis |
| id | usm-44031 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T17:55:29Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-440312019-04-12T05:26:16Z http://eprints.usm.my/44031/ Bedload Transport Of Small Rivers In Malaysia 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 NonPeerReviewed application/pdf en http://eprints.usm.my/44031/1/Zahra%20Zangeneh%20Sirdari24.pdf Sirdari, Zahra Zangeneh (2013) Bedload Transport Of Small Rivers In Malaysia. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | TA1-2040 Engineering (General). Civil engineering (General) Sirdari, Zahra Zangeneh Bedload Transport Of Small Rivers In Malaysia |
| title | 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_short | Bedload Transport Of Small Rivers In Malaysia |
| title_sort | bedload transport of small rivers in malaysia |
| topic | TA1-2040 Engineering (General). Civil engineering (General) |
| url | http://eprints.usm.my/44031/ http://eprints.usm.my/44031/1/Zahra%20Zangeneh%20Sirdari24.pdf |