Abstract: In order to optimize the layout of water quality monitoring section of river, lake and reservoir effectively, a new method for water quality monitoring section optimization was proposed based on comprehensive hierarchical clustering (CHC). A normalized matrix was constructed according to the original monitoring data of water quality. The method calculated the similarity to obtain the affinity-disaffinity relationship among the monitoring variables by 5 different distance algorithms, and then clustered the data set automatically by 4 different connection algorithms. Moreover, taking the correlation coefficient as the evaluation criteria, optimal hierarchical clustering algorithm was selected. With the generation of corresponding optimal clustering tree matrix, the section monitoring sites were set optimally to obtain the monitoring data reflecting the water quality of whole area, thus the monitoring water quality could be more effectively. The experimental results show that this method is practical and integrated highly, thus has good prospect.

Key words: water quality monitoring, section optimization, comprehensive hierarchical clustering, evaluation criteria of correlation coefficient