Characterization of spatial patterns in river water quality using chemometric pattern recognition techniques

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• 作者：Nabeel M. Gazzaz ; ^{NabeelMGazzaz@Yahoo.com} ; Mohd Kamil Yusoff ; Mohammad Firuz Ramli ; Ahmad Zaharin Aris ; Hafizan Juahir
• 关键词：asl ; above sea level ; CA ; cluster analysis ; DF ; discriminant function ; DFA ; discriminant function analysis ; DoE ; Department of Environment ; Malaysia ; FA ; factor analysis ; HCA ; hierarchical cluster analysis ; HACA ; hierarchical agglomerative cluster analysi
• 刊名：Marine Pollution Bulletin
• 出版年：2012
• 出版时间：April, 2012
• 年：2012
• 卷：64
• 期：4
• 页码：688-698
• 全文大小：1K

文摘

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.