Integrated GIS and multivariate statistical approach for spatial and temporal variability analysis for lake water quality index

• Main Authors: Subramaniam P., Ahmed A.N., Fai C.M., Abdul Malek M., Kumar P., Huang Y.F., Sherif M., Elshafie A.
• Other Authors: 57223344990
• Format: Article
• Published: Cogent OA 2024
• Subjects: cluster analysis; discriminant analysis; Kenyir Lake; principal component analysis; water quality

It is critical to monitor water quality to keep water bodies ecologically healthy and facilitate the sustainable development of Kenyir Lake. Water quality differs temporally and spatially and is affected by several factors. Typically, water quality inspection systems are cost- and labour-intensive depending on water quality indicator count and sampling frequency. Optimising the frequency and location of water quality sampling is crucial. This study focused on collecting water samples from 22 locations in Kenyir Lake during different seasons (normal, dry, and wet). The study aimed to assess the spatial and temporal variations in the water quality of Kenyir Lake based on multivariate statistical methods. In this study, the following water quality parameters were selected for analysis: temperature, dissolved oxygen (DO), pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), and ammoniacal nitrogen (NH3-N). In addition, a water quality index was also calculated. GIS software was used to assess water quality data, and various multivariate statistical methods like cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) were employed. The outcome shows minor spatial differences concerning Kenyir Lake