Spatial assessment of Langat river water quality using chemometrics

The present study deals with the assessment of Langat River water quality with some chemometrics approaches such as cluster and discriminant analysis coupled with an artificial neural network (ANN). The data used in this study were collected from seven monitoring stations under the river water quali...

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Main Authors:	Juahir, Hafizan, Md Zain, Sharifuddin, Aris, Ahmad Zaharin, Yusoff, Mohd Kamil, Mokhtar, Mazlin
Format:	Article
Language:	English English
Published:	Royal Society of Chemistry 2010
Online Access:	http://psasir.upm.edu.my/id/eprint/16792/ http://psasir.upm.edu.my/id/eprint/16792/ http://psasir.upm.edu.my/id/eprint/16792/1/Spatial%20assessment%20of%20Langat%20river%20water%20quality%20using%20chemometrics.pdf

id	upm-16792
recordtype	eprints
spelling	upm-167922015-10-08T04:46:56Z http://psasir.upm.edu.my/id/eprint/16792/ Spatial assessment of Langat river water quality using chemometrics Juahir, Hafizan Md Zain, Sharifuddin Aris, Ahmad Zaharin Yusoff, Mohd Kamil Mokhtar, Mazlin The present study deals with the assessment of Langat River water quality with some chemometrics approaches such as cluster and discriminant analysis coupled with an artificial neural network (ANN). The data used in this study were collected from seven monitoring stations under the river water quality monitoring program by the Department of Environment (DOE) from 1995 to 2002. Twenty three physico-chemical parameters were involved in this analysis. Cluster analysis successfully clustered the Langat River into three major clusters, namely high, moderate and less pollution regions. Discriminant analysis identified seven of the most significant parameters which contribute to the high variation of Langat River water quality, namely dissolved oxygen, biological oxygen demand, pH, ammoniacal nitrogen, chlorine, E. coli, and coliform. Discriminant analysis also plays an important role as an input selection parameter for an ANN of spatial prediction (pollution regions). The ANN showed better prediction performance in discriminating the regional area with an excellent percentage of correct classification compared to discriminant analysis. Multivariate analysis, coupled with ANN, is proposed, which could help in decision making and problem solving in the local environment. Royal Society of Chemistry 2010 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/16792/1/Spatial%20assessment%20of%20Langat%20river%20water%20quality%20using%20chemometrics.pdf Juahir, Hafizan and Md Zain, Sharifuddin and Aris, Ahmad Zaharin and Yusoff, Mohd Kamil and Mokhtar, Mazlin (2010) Spatial assessment of Langat river water quality using chemometrics. Journal of Environmental Monitoring, 12 (1). pp. 287-295. ISSN 1464-0325 10.1039/B907306J English
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Putra Malaysia
building	UPM Institutional Repository
collection	Online Access
language	English English
description	The present study deals with the assessment of Langat River water quality with some chemometrics approaches such as cluster and discriminant analysis coupled with an artificial neural network (ANN). The data used in this study were collected from seven monitoring stations under the river water quality monitoring program by the Department of Environment (DOE) from 1995 to 2002. Twenty three physico-chemical parameters were involved in this analysis. Cluster analysis successfully clustered the Langat River into three major clusters, namely high, moderate and less pollution regions. Discriminant analysis identified seven of the most significant parameters which contribute to the high variation of Langat River water quality, namely dissolved oxygen, biological oxygen demand, pH, ammoniacal nitrogen, chlorine, E. coli, and coliform. Discriminant analysis also plays an important role as an input selection parameter for an ANN of spatial prediction (pollution regions). The ANN showed better prediction performance in discriminating the regional area with an excellent percentage of correct classification compared to discriminant analysis. Multivariate analysis, coupled with ANN, is proposed, which could help in decision making and problem solving in the local environment.
format	Article
author	Juahir, Hafizan Md Zain, Sharifuddin Aris, Ahmad Zaharin Yusoff, Mohd Kamil Mokhtar, Mazlin
spellingShingle	Juahir, Hafizan Md Zain, Sharifuddin Aris, Ahmad Zaharin Yusoff, Mohd Kamil Mokhtar, Mazlin Spatial assessment of Langat river water quality using chemometrics
author_facet	Juahir, Hafizan Md Zain, Sharifuddin Aris, Ahmad Zaharin Yusoff, Mohd Kamil Mokhtar, Mazlin
author_sort	Juahir, Hafizan
title	Spatial assessment of Langat river water quality using chemometrics
title_short	Spatial assessment of Langat river water quality using chemometrics
title_full	Spatial assessment of Langat river water quality using chemometrics
title_fullStr	Spatial assessment of Langat river water quality using chemometrics
title_full_unstemmed	Spatial assessment of Langat river water quality using chemometrics
title_sort	spatial assessment of langat river water quality using chemometrics
publisher	Royal Society of Chemistry
publishDate	2010
url	http://psasir.upm.edu.my/id/eprint/16792/ http://psasir.upm.edu.my/id/eprint/16792/ http://psasir.upm.edu.my/id/eprint/16792/1/Spatial%20assessment%20of%20Langat%20river%20water%20quality%20using%20chemometrics.pdf
first_indexed	2018-09-07T13:48:19Z
last_indexed	2018-09-07T13:48:19Z
_version_	1610956773121851392

Spatial assessment of Langat river water quality using chemometrics

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