Determination Of Flow Resistance Coefficient Using Multiple Linear Regression And Genetic Expression Programming

The use of the accurate value of the roughness coefficient for flow resistance in the open channel is a necessity in computation. The engineers have used a number of flow resistance equations involving grain roughness, form roughness and a combination of both. However, Manning’s equation has been wi...

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Main Author:	Abdul Ghaffar, Ahmad Bakri
Format:	Thesis
Language:	English
Published:	2019
Subjects:	T Technology
Online Access:	http://eprints.usm.my/48561/1/Determination%20Of%20Flow%20Resistance%20Coefficient%20Using%20Multiple%20Linear%20Regression%20And%20Genetic%20Expression%20Programming.pdf
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spelling	my-usm-ep.485612021-11-17T03:42:11Z Determination Of Flow Resistance Coefficient Using Multiple Linear Regression And Genetic Expression Programming 2019-07-01 Abdul Ghaffar, Ahmad Bakri T Technology TC401-506 River, lake, and water-supply engineering (General) The use of the accurate value of the roughness coefficient for flow resistance in the open channel is a necessity in computation. The engineers have used a number of flow resistance equations involving grain roughness, form roughness and a combination of both. However, Manning’s equation has been widely used internationally for predicting roughness values in natural channels. In river engineering, Manning’s roughness coefficient, n, has been used widely in river hydraulic models. The procedure for selecting n is subjective and requires judgment and skill that is developed primarily through experience apart from knowing the factors which affect the values of n. Since flow and boundary roughness vary with existing river conditions, a model of some form must be developed to evaluate n for rivers in Malaysia. This research has been carried out on four rivers namely the river basins of Kinta River, Langat River, Muda River, and Kurau River. A total of 501 data have been collected at the four-river basin. Assessment of the existing equations i.e. Strickler, Limerinos, Bruschin, Griffith, Bray, Jarrett, Julien, and Ab. Ghani was carried out. Based on the evaluation of the selected equations, Jarret (1984) and Ab Ghani et al. (2007) equation are recommended to predict flow discharge for the sandy rivers such as Kinta River and Langat River. For gravel rivers such as Muda River and Kurau River, Jarret (1984), Bruschin (1985) and Limerinos (1970) equation are recommended to predict flow discharge. The development of new equations was carried out in the present study using Multiple Linear Regression (MLR) and Genetic. Expression Programming (GEP). The MLR-based equation (Equation 4.4) is recommended while GEP-based equation (Equation 4.6) is greatly recommended. The development of flow rating curve for the rivers in the present study (Figures 4.16 to 4.19) validate the applicability of Equations 4.4 and 4.6 in calculating the flow discharge which can be used to predict low and high flows for rivers in Malaysia. 2019-07 Thesis http://eprints.usm.my/48561/ http://eprints.usm.my/48561/1/Determination%20Of%20Flow%20Resistance%20Coefficient%20Using%20Multiple%20Linear%20Regression%20And%20Genetic%20Expression%20Programming.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Penyelidikan Kejuruteraan Sungai Dan Saliran Bandar (REDAC)
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	T Technology T Technology
spellingShingle	T Technology T Technology Abdul Ghaffar, Ahmad Bakri Determination Of Flow Resistance Coefficient Using Multiple Linear Regression And Genetic Expression Programming
description	The use of the accurate value of the roughness coefficient for flow resistance in the open channel is a necessity in computation. The engineers have used a number of flow resistance equations involving grain roughness, form roughness and a combination of both. However, Manning’s equation has been widely used internationally for predicting roughness values in natural channels. In river engineering, Manning’s roughness coefficient, n, has been used widely in river hydraulic models. The procedure for selecting n is subjective and requires judgment and skill that is developed primarily through experience apart from knowing the factors which affect the values of n. Since flow and boundary roughness vary with existing river conditions, a model of some form must be developed to evaluate n for rivers in Malaysia. This research has been carried out on four rivers namely the river basins of Kinta River, Langat River, Muda River, and Kurau River. A total of 501 data have been collected at the four-river basin. Assessment of the existing equations i.e. Strickler, Limerinos, Bruschin, Griffith, Bray, Jarrett, Julien, and Ab. Ghani was carried out. Based on the evaluation of the selected equations, Jarret (1984) and Ab Ghani et al. (2007) equation are recommended to predict flow discharge for the sandy rivers such as Kinta River and Langat River. For gravel rivers such as Muda River and Kurau River, Jarret (1984), Bruschin (1985) and Limerinos (1970) equation are recommended to predict flow discharge. The development of new equations was carried out in the present study using Multiple Linear Regression (MLR) and Genetic. Expression Programming (GEP). The MLR-based equation (Equation 4.4) is recommended while GEP-based equation (Equation 4.6) is greatly recommended. The development of flow rating curve for the rivers in the present study (Figures 4.16 to 4.19) validate the applicability of Equations 4.4 and 4.6 in calculating the flow discharge which can be used to predict low and high flows for rivers in Malaysia.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Abdul Ghaffar, Ahmad Bakri
author_facet	Abdul Ghaffar, Ahmad Bakri
author_sort	Abdul Ghaffar, Ahmad Bakri
title	Determination Of Flow Resistance Coefficient Using Multiple Linear Regression And Genetic Expression Programming
title_short	Determination Of Flow Resistance Coefficient Using Multiple Linear Regression And Genetic Expression Programming
title_full	Determination Of Flow Resistance Coefficient Using Multiple Linear Regression And Genetic Expression Programming
title_fullStr	Determination Of Flow Resistance Coefficient Using Multiple Linear Regression And Genetic Expression Programming
title_full_unstemmed	Determination Of Flow Resistance Coefficient Using Multiple Linear Regression And Genetic Expression Programming
title_sort	determination of flow resistance coefficient using multiple linear regression and genetic expression programming
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Penyelidikan Kejuruteraan Sungai Dan Saliran Bandar (REDAC)
publishDate	2019
url	http://eprints.usm.my/48561/1/Determination%20Of%20Flow%20Resistance%20Coefficient%20Using%20Multiple%20Linear%20Regression%20And%20Genetic%20Expression%20Programming.pdf
_version_	1747821947460255744

Determination Of Flow Resistance Coefficient Using Multiple Linear Regression And Genetic Expression Programming

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