Spatial assessment of Langat river water quality using chemometrics

Hafizan Juahir, Sharifuddin Md Zain, Ahmad Zaharin Aris, Mohd Kamil Yusoff, Mazlin Mokhtar

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)287-295
Number of pages9
JournalJournal of Environmental Monitoring
Volume12
Issue number1
DOIs
Publication statusPublished - 2010

Fingerprint

Water Quality
Discriminant Analysis
Discriminant analysis
discriminant analysis
Rivers
artificial neural network
Water quality
river water
Neural networks
water quality
Cluster analysis
Cluster Analysis
cluster analysis
Pollution
Biological Oxygen Demand Analysis
pollution
Monitoring
Chlorine
biochemical oxygen demand
Dissolved oxygen

ASJC Scopus subject areas

  • Management, Monitoring, Policy and Law
  • Public Health, Environmental and Occupational Health

Cite this

Spatial assessment of Langat river water quality using chemometrics. / Juahir, Hafizan; Zain, Sharifuddin Md; Aris, Ahmad Zaharin; Yusoff, Mohd Kamil; Mokhtar, Mazlin.

In: Journal of Environmental Monitoring, Vol. 12, No. 1, 2010, p. 287-295.

Research output: Contribution to journalArticle

Juahir, Hafizan ; Zain, Sharifuddin Md ; Aris, Ahmad Zaharin ; Yusoff, Mohd Kamil ; Mokhtar, Mazlin. / Spatial assessment of Langat river water quality using chemometrics. In: Journal of Environmental Monitoring. 2010 ; Vol. 12, No. 1. pp. 287-295.
@article{6e2cdc8476994de5a137430a31eb7c27,
title = "Spatial assessment of Langat river water quality using chemometrics",
abstract = "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.",
author = "Hafizan Juahir and Zain, {Sharifuddin Md} and Aris, {Ahmad Zaharin} and Yusoff, {Mohd Kamil} and Mazlin Mokhtar",
year = "2010",
doi = "10.1039/b907306j",
language = "English",
volume = "12",
pages = "287--295",
journal = "Environmental Sciences: Processes and Impacts",
issn = "2050-7887",
publisher = "Royal Society of Chemistry",
number = "1",

}

TY - JOUR

T1 - Spatial assessment of Langat river water quality using chemometrics

AU - Juahir, Hafizan

AU - Zain, Sharifuddin Md

AU - Aris, Ahmad Zaharin

AU - Yusoff, Mohd Kamil

AU - Mokhtar, Mazlin

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=77249106604&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77249106604&partnerID=8YFLogxK

U2 - 10.1039/b907306j

DO - 10.1039/b907306j

M3 - Article

C2 - 20082024

AN - SCOPUS:77249106604

VL - 12

SP - 287

EP - 295

JO - Environmental Sciences: Processes and Impacts

JF - Environmental Sciences: Processes and Impacts

SN - 2050-7887

IS - 1

ER -