In Pasir Gudang, an accelerated industry-based economy has caused a tremendous increase and diversity of water contamination. The application of multivariate statistical techniques can identify factors that influence water systems and is a valuable tool for managing water resources. Therefore, this study presents spatial evaluation and the elucidation of inordinate complex data for 32 parameters from 25 sampling points spanning 20 rivers across Pasir Gudang, summing up to 1500 observations between 2015-2019. Hierarchical cluster analysis with the K-means method grouped the rivers into two main clusters, i.e., proportionately low polluted rivers for Cluster 1 (C1) and high polluted rivers for Cluster 2 (C2), based on the similitude of water quality profiles. The discriminant analysis applied to the cluster resulted in a data reduction from 32 to 7 parameters (Cl, Cd, S, OG, temperature, BOD, and pH) with a 99.5% correct categorization in spatial analysis. Hence, element complexity was reduced to a few criteria accountable for large water quality differences between C1 and C2. The principal component analysis produced 6 and 7 principal components after rotation for C1 and C2, respectively, where total variance was 62.48% and 66.85%. In addition, several sub-clusters were identified; two from C1 and three from C2, based on the principal contributing components. These results show that the functionality of multivariate techniques can be effectively used to identify spatial water characteristics and pollution sources. The outcomes of this study may benefit legislators in managing rivers within Pasir Gudang.

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