River Water Quality Zoning with C- means Clustering A case study: the quality of Zayandehroud River

The application of fuzzy c-means clustering approach for the interpretation of a large and complex data matrix is presented in this study. The dataset consists of six parameters, have been monitored on 8 key sampling sites for Zayandehroud river. In this paper, a methodology is presented for this purpose using fuzzy c –means clustering method. The efficiency of this clustering method was evaluated using water quality data gathered from the monitoring sampling points along Zayandehroud river. Validity of determinate clusters was determined with partition coefficient (PC) and partition entropy (PE). Also a Fuzzy Industrial Water Quality Index (FIWQI) was calculated for center of each cluster. Most of the months fuzzy clustering showed two different groups of the sampling sites, reflecting the different physicochemical characteristics and pollution levels of the study area. The results show that the proposed methodology was informative for decision-making and to help river water quality management.

River Water Quality Zoning with C- means Clustering A case study: the quality of Zayandehroud River Keywords:

c-means clustering , water quality zoning , industrial water , Zayandehroud