title: Characterization of spatial patterns in river water quality using chemometric pattern recognition techniques.
abstract: This study employed three chemometric data mining techniques (factor analysis (FA), cluster analysis (CA), and discriminant analysis (DA)) to identify the latent structure of a water quality (WQ) dataset pertaining to Kinta River (Malaysia) and to classify eight WQ monitoring stations along the river into groups of similar WQ characteristics. FA identified the WQ parameters responsible for variations in Kinta River's WQ and accentuated the roles of weathering and surface runoff in determining the river's WQ. CA grouped the monitoring locations into a cluster of low levels of water pollution (the two uppermost monitoring stations) and another of relatively high levels of river pollution (the mid-, and down-stream stations). DA confirmed these clusters and produced a discriminant function which can predict the cluster membership of new and/or unknown samples. These chemometric techniques highlight the potential for reasonably reducing the number of WQVs and monitoring stations for long-term monitoring purposes.
authors.affiliation: Department of Environmental Science, Faculty of Environmental Studies, University Putra Malaysia, 43400 Serdang, Selangur Darul Ehsan, Malaysia. NabeelMGazzaz@Yahoo.com
authors.forename: Nabeel M
authors.initials: NM
authors.lastname: Gazzaz
authors.forename: Mohd Kamil
authors.initials: MK
authors.lastname: Yusoff
authors.forename: Mohammad Firuz
authors.initials: MF
authors.lastname: Ramli
authors.forename: Ahmad Zaharin
authors.initials: AZ
authors.lastname: Aris
authors.forename: Hafizan
authors.initials: H
authors.lastname: Juahir
mesh_terms: D057225:Data Mining; D004784:Environmental Monitoring / Q000379:methods*; D008296:Malaysia; D045483:Rivers; D014874:Water Pollutants, Chemical / Q000032:analysis*; D060753:Water Quality
chemical_list: D014874:Water Pollutants, Chemical