Water Quality Assessment of Muar River Using Environmetric Techniques and Artificial Neural Networks

The pollution discharge has influence the chemical composition of Muar River where studied was carried out using the Environmetric Techniques and the Artificial Neural Networks (ANNs) model. Environmetric method, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA),...

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Main Author:	Putri Shazlia, Rosman
Format:	Conference or Workshop Item
Language:	English
Published:	2015
Subjects:	T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/10221/ http://umpir.ump.edu.my/id/eprint/10221/1/Water%20Quality%20Assessment%20of%20Muar%20River%20Using%20Environmetric%20Techniques%20and%20Artificial%20Neural%20Networks.pdf

id	ump-10221
recordtype	eprints
spelling	ump-102212018-02-05T07:19:44Z http://umpir.ump.edu.my/id/eprint/10221/ Water Quality Assessment of Muar River Using Environmetric Techniques and Artificial Neural Networks Putri Shazlia, Rosman T Technology (General) The pollution discharge has influence the chemical composition of Muar River where studied was carried out using the Environmetric Techniques and the Artificial Neural Networks (ANNs) model. Environmetric method, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA) and the factor analysis (FA) to study the spatial variations of water quality variables and to determine the origin sources of pollution. ANNs model was used to predict linear relationship between water quality variables, the most significant variables that influence Muar River as well as sources of apportionment pollution. HACA observed three spatial clusters were formed. DA managed to discriminate 16 and 19 water quality variables thru forward and backward stepwise. Eight principal components were found responsible for the data structure and 67.7% of the total variance of the data set in PCA/FA analysis. ANNs analysis, strong relationship correlation was observed between salinity, conductivity, DS, TS, Cl, Ca, K, Mg and Na (r = 0.954 to 0.997), moderate relationship observed between COD and E.coli (r = 0.449) and Cd and Pb (r = 0.492) and others variables have no significant correlation. pH was the most significant variables (51.6%) and Fe was less significant variables (-0.52%). The major sources of pollution of the river were due to natural degradation / natural process that affecting the pH value of the river. Other pollution contribution was from anthropogenic sources such as agricultural runoff, industrial discharge, domestic waste, natural erosion, livestock farming and present of nitrogenous species. The ANNs showed better prediction in identified most significant variable compare to Environmetric techniques. ANNs is an effective tool in decision making and problem solving for local/global environmental issues. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/10221/1/Water%20Quality%20Assessment%20of%20Muar%20River%20Using%20Environmetric%20Techniques%20and%20Artificial%20Neural%20Networks.pdf Putri Shazlia, Rosman (2015) Water Quality Assessment of Muar River Using Environmetric Techniques and Artificial Neural Networks. In: Proceeding of Engineering Technology International Conference (ETIC 2015), 10-11 August 2015 , Bali, Indonesia. pp. 1-8..
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Putri Shazlia, Rosman Water Quality Assessment of Muar River Using Environmetric Techniques and Artificial Neural Networks
description	The pollution discharge has influence the chemical composition of Muar River where studied was carried out using the Environmetric Techniques and the Artificial Neural Networks (ANNs) model. Environmetric method, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA) and the factor analysis (FA) to study the spatial variations of water quality variables and to determine the origin sources of pollution. ANNs model was used to predict linear relationship between water quality variables, the most significant variables that influence Muar River as well as sources of apportionment pollution. HACA observed three spatial clusters were formed. DA managed to discriminate 16 and 19 water quality variables thru forward and backward stepwise. Eight principal components were found responsible for the data structure and 67.7% of the total variance of the data set in PCA/FA analysis. ANNs analysis, strong relationship correlation was observed between salinity, conductivity, DS, TS, Cl, Ca, K, Mg and Na (r = 0.954 to 0.997), moderate relationship observed between COD and E.coli (r = 0.449) and Cd and Pb (r = 0.492) and others variables have no significant correlation. pH was the most significant variables (51.6%) and Fe was less significant variables (-0.52%). The major sources of pollution of the river were due to natural degradation / natural process that affecting the pH value of the river. Other pollution contribution was from anthropogenic sources such as agricultural runoff, industrial discharge, domestic waste, natural erosion, livestock farming and present of nitrogenous species. The ANNs showed better prediction in identified most significant variable compare to Environmetric techniques. ANNs is an effective tool in decision making and problem solving for local/global environmental issues.
format	Conference or Workshop Item
author	Putri Shazlia, Rosman
author_facet	Putri Shazlia, Rosman
author_sort	Putri Shazlia, Rosman
title	Water Quality Assessment of Muar River Using Environmetric Techniques and Artificial Neural Networks
title_short	Water Quality Assessment of Muar River Using Environmetric Techniques and Artificial Neural Networks
title_full	Water Quality Assessment of Muar River Using Environmetric Techniques and Artificial Neural Networks
title_fullStr	Water Quality Assessment of Muar River Using Environmetric Techniques and Artificial Neural Networks
title_full_unstemmed	Water Quality Assessment of Muar River Using Environmetric Techniques and Artificial Neural Networks
title_sort	water quality assessment of muar river using environmetric techniques and artificial neural networks
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/10221/ http://umpir.ump.edu.my/id/eprint/10221/1/Water%20Quality%20Assessment%20of%20Muar%20River%20Using%20Environmetric%20Techniques%20and%20Artificial%20Neural%20Networks.pdf
first_indexed	2023-09-18T22:09:36Z
last_indexed	2023-09-18T22:09:36Z
_version_	1777414938589921280

Water Quality Assessment of Muar River Using Environmetric Techniques and Artificial Neural Networks

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