Classification of river water quality using multivariate analysis

• Main Authors: Azhar, Shah Christirani, Aris, Ahmad Zaharin, Yusoff, Mohd Kamil, Ramli, Mohammad Firuz
• Format: Article
• Language: English
• Published: Elsevier 2015
• Online Access: http://psasir.upm.edu.my/id/eprint/47836/1/47836.pdf; http://psasir.upm.edu.my/id/eprint/47836/; http://www.sciencedirect.com/science/article/pii/S1878029615006088

The classification of river water quality is a useful way of reporting the water quality status of a river to control water pollution in monitored regions. The main objective of this study is to classify the water quality of the Muda River basin (Malaysia) using nine monitoring stations. This study utilised multivariate analysis of cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA). CA and PCA identified two different clusters (classes) that reflect the different water quality characteristics of the water systems. DA validated these clusters and produced a discriminant function (DF) that can predict the cluster membership of new samples. The classification generated by the multivariate analysis is consistent with those made by the Department of Environment (DOE). This study demonstrated that multivariate statistical techniques are effective for river water classification.