Streambank erosion prediction using empirical model for natural river channels / Azlinda Saadon

Streambank erosion is commonly associated with river meandering initiation and development, through width adjustment and planform evolution. It consists of two types of erosion mechanism; basal erosion due to fluvial hydraulic force and bank failure under the influence of gravity. These processes re...

Full description

Bibliographic Details
Main Author:	Saadon, Azlinda
Format:	Book Section
Language:	English
Published:	Institute of Graduate Studies, UiTM 2018
Subjects:	Environmental protection Sewage disposal systems
Online Access:	http://ir.uitm.edu.my/id/eprint/20495/ http://ir.uitm.edu.my/id/eprint/20495/1/ABS_AZLINDA%20SAADON%20TDRA%20VOL%2013%20IGS_18.pdf

id	uitm-20495
recordtype	eprints
spelling	uitm-204952018-06-12T06:21:42Z http://ir.uitm.edu.my/id/eprint/20495/ Streambank erosion prediction using empirical model for natural river channels / Azlinda Saadon Saadon, Azlinda Environmental protection Sewage disposal systems Streambank erosion is commonly associated with river meandering initiation and development, through width adjustment and planform evolution. It consists of two types of erosion mechanism; basal erosion due to fluvial hydraulic force and bank failure under the influence of gravity. These processes require an intergration between soil-water interactions to properly understand the factors constitutes to streambank erosion and its impact to major scouring. A study was undertaken to explore the rates of streambank erosion and the factors of streambank erosion. Fieldwork investigation technique was conducted in the quantification of streambank erosion rates. Field data has been extracted from two streams which have been identified as the area susceptible to streambank erosion, namely, Sg. Bernam and Sg. Lui, both located in states of Selangor, Malaysia. Measurement of streambank erosion rates has been conducted using short-time scale field technique. Conventional erosion pin arrays, repeated cross-profiling and vertical streambank profiling were employed in order to obtain the streambank erosion rates. The fieldwork data obtained from fieldwork erosion monitoring served as a pilot inventory streambank erosion data for both site areas. Dimensional analysis is performed to establish the factors governing streambank erosion. Two functional relationships addressing streambank erosion rates incorporating factors of hydraulic characteristics, resistance to the soil, streambank gemotery, grain and sediment resistance were established using Buckingham PI theorem using two sets of repeating variables. Selection of the most significant parameters constitutes to streambank erosion rates is obtained from the analysis. Results from the analysis concluded eleven dimensionless parameters as factors governing streambank erosion rates. Further objective focused on the development of newly streambank erosion expression using empirical approach. A total of 318 data were used in the model development. Three methods have been employed in the model development, namely, (i) Statistical approach using multiple linear, nonlinear regressions, and logartithmic transformation function; (ii) Linear Least Squares (LLS) solution for Nonlinear Autoregressive Exogenous (NARX) using QR factorization parameter estimator; and, (iii) Artificial Neural Network (ANN) method. The established models were validated to assess their performaces in predicting the rates of streambank erosion. 176 data were used in the model validation. The performance of the developed equations was assessed using three criteria, namely, (i) discrepancy ratio (ratio of predicted values to the measured values); (ii) statistical test analysis; and, (iii) graphical analysis. Validation of the developed equations confirmed that ANN model gave very good prediction where all data lie within the line of perfect agreement of the discrepancy ratio limit of 0.5 to 2.0, for both Model no. 1 and 2. Other models (statistical and NARX model) predicted equally good performance ranging from 70% - 90% accuracy. Further analysis is required to test the developed model specifically for different river characteristics. However, the availability of data is a hindrance and to draw these findings, further recommendations are summarized for the validity of the derived equations. Institute of Graduate Studies, UiTM 2018 Book Section PeerReviewed text en http://ir.uitm.edu.my/id/eprint/20495/1/ABS_AZLINDA%20SAADON%20TDRA%20VOL%2013%20IGS_18.pdf Saadon, Azlinda (2018) Streambank erosion prediction using empirical model for natural river channels / Azlinda Saadon. In: The Doctoral Research Abstracts. IPSis Biannual Publication, 13 (13). Institute of Graduate Studies, UiTM, Shah Alam.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Environmental protection Sewage disposal systems
spellingShingle	Environmental protection Sewage disposal systems Saadon, Azlinda Streambank erosion prediction using empirical model for natural river channels / Azlinda Saadon
description	Streambank erosion is commonly associated with river meandering initiation and development, through width adjustment and planform evolution. It consists of two types of erosion mechanism; basal erosion due to fluvial hydraulic force and bank failure under the influence of gravity. These processes require an intergration between soil-water interactions to properly understand the factors constitutes to streambank erosion and its impact to major scouring. A study was undertaken to explore the rates of streambank erosion and the factors of streambank erosion. Fieldwork investigation technique was conducted in the quantification of streambank erosion rates. Field data has been extracted from two streams which have been identified as the area susceptible to streambank erosion, namely, Sg. Bernam and Sg. Lui, both located in states of Selangor, Malaysia. Measurement of streambank erosion rates has been conducted using short-time scale field technique. Conventional erosion pin arrays, repeated cross-profiling and vertical streambank profiling were employed in order to obtain the streambank erosion rates. The fieldwork data obtained from fieldwork erosion monitoring served as a pilot inventory streambank erosion data for both site areas. Dimensional analysis is performed to establish the factors governing streambank erosion. Two functional relationships addressing streambank erosion rates incorporating factors of hydraulic characteristics, resistance to the soil, streambank gemotery, grain and sediment resistance were established using Buckingham PI theorem using two sets of repeating variables. Selection of the most significant parameters constitutes to streambank erosion rates is obtained from the analysis. Results from the analysis concluded eleven dimensionless parameters as factors governing streambank erosion rates. Further objective focused on the development of newly streambank erosion expression using empirical approach. A total of 318 data were used in the model development. Three methods have been employed in the model development, namely, (i) Statistical approach using multiple linear, nonlinear regressions, and logartithmic transformation function; (ii) Linear Least Squares (LLS) solution for Nonlinear Autoregressive Exogenous (NARX) using QR factorization parameter estimator; and, (iii) Artificial Neural Network (ANN) method. The established models were validated to assess their performaces in predicting the rates of streambank erosion. 176 data were used in the model validation. The performance of the developed equations was assessed using three criteria, namely, (i) discrepancy ratio (ratio of predicted values to the measured values); (ii) statistical test analysis; and, (iii) graphical analysis. Validation of the developed equations confirmed that ANN model gave very good prediction where all data lie within the line of perfect agreement of the discrepancy ratio limit of 0.5 to 2.0, for both Model no. 1 and 2. Other models (statistical and NARX model) predicted equally good performance ranging from 70% - 90% accuracy. Further analysis is required to test the developed model specifically for different river characteristics. However, the availability of data is a hindrance and to draw these findings, further recommendations are summarized for the validity of the derived equations.
format	Book Section
author	Saadon, Azlinda
author_facet	Saadon, Azlinda
author_sort	Saadon, Azlinda
title	Streambank erosion prediction using empirical model for natural river channels / Azlinda Saadon
title_short	Streambank erosion prediction using empirical model for natural river channels / Azlinda Saadon
title_full	Streambank erosion prediction using empirical model for natural river channels / Azlinda Saadon
title_fullStr	Streambank erosion prediction using empirical model for natural river channels / Azlinda Saadon
title_full_unstemmed	Streambank erosion prediction using empirical model for natural river channels / Azlinda Saadon
title_sort	streambank erosion prediction using empirical model for natural river channels / azlinda saadon
publisher	Institute of Graduate Studies, UiTM
publishDate	2018
url	http://ir.uitm.edu.my/id/eprint/20495/ http://ir.uitm.edu.my/id/eprint/20495/1/ABS_AZLINDA%20SAADON%20TDRA%20VOL%2013%20IGS_18.pdf
first_indexed	2023-09-18T23:04:44Z
last_indexed	2023-09-18T23:04:44Z
_version_	1777418407396769792

Streambank erosion prediction using empirical model for natural river channels / Azlinda Saadon

Similar Items