Research on Optimization of Water Quality Monitoring Sites Using Principal Component Analysis and Cluster Analysis

The layout of water quality monitoring sites of Changzhou could be optimized for the purpose of reducing the associated expense. Based on the monitoring data during the year of 2001-2004, principal component analysis (PCA) and cluster analysis (CA) were combined to be applied for the optimization of monitoring sites. Main results showed that three PCs contributed to the cumulative variance greater than 85% and 16 water quality monitoring sites of Changzhou could be classified in 7 clusters. It was concluded that PCA and CAhad a great potential for the management of water quality monitoring systems. This method can be applied to monitoring sites optimizing design of air, soil and noise environment.