Integrating Principle Component Analysis and Canonical Correlation Analysis for Monitoring Water Quality in Storage Reservoir

Abstract

Water quality monitoring is of great importance in managing drinking water lakes or reservoirs. However, developing such an effective monitoring system used in evaluating water quality is difficult, due to spatial and temporal variation in water quality that are usually hard to interpret. Principle component analysis (PCA) and canonical correlation analysis (CCA) techniques were used in this study to determine the principle water parameters, and explore the relationship between the physical and chemical water parameters, respectively in Macau Main Storage Reservoir (MSR) that is experiencing algal bloom in recent years. Twenty-eight water parameters including physical and chemical parameters were monitored each month from Jan 2001 to Dec 2010. The results showed that using PCA, six of the water quality parameters were found less important in explaining the monthly variation of the water quality and thus excluded from the further CCA, while applying CCA, the first six canonical correlations were 0.998, 0.988, 0.937, 0.778, 0.606 and 0.453, indicating that electro-conductivity and chloride compounds were the dominant (highly scored) variables of the physical parameters and chemical parameters, respectively. These results are helpful in understanding the main physical and chemical parameters involved in MSR, thus improving the water quality monitoring system in drinking water reservoirs.