A Fuzzy-Integrated Approach to Estimate Water Quality in Surface Water Bodies

The purpose of this article is to evaluate the quality of a reservoir’s surface water through the application of fuzzy-logic methodologies. Specifically, fuzzy-clustering (Fuzzy C-Means) and fuzzy-regression techniques are employed to classify and model the physicochemical and microbiological parameters of the water. The analyzed dataset indicates a particularly close and interesting relationship between the fuzzy-regression results and the fuzzy c-means-clustering results. Hence, the centers of the clusters lie very close to the central value of the produced fuzzy-regression band. This behavior appears when four clusters are used, while the evaluation (performed using the Partition Coefficient (VPC) and the Partition Entropy (VPE) indices) also indicates that four clusters is the most appropriate choice.