Comparison of FE and FLS Model for Surface Water Quality Assessment

AbstractFresh water scarcity ranks among the most urgent environmental challenges in this century, its quality increasingly being threatened by man, necessitating continuous monitoring of this valued resource. The present study focuses on the development of a methodology for surface water quality assessment incorporating uncertainty in the data, by defining fuzzy sets and an objective assignment of weightages to water quality parameters by Entropy theory. Fuzzy comprehensive evaluation was used to generate membership functions and in turn fuzzy relationship matrix was developed. The weight of each associated parameter was computed using Entropy concept which was combined with the so developed fuzzy relationship matrix to define assessment coefficient which led to overall water quality and station-wise quality assessment. The newly developed Fuzzy-Entropy (FE) model is compared with the traditional method of Fuzzy Logic system (FLS) model. The developed FE and FLS models were applied to Keno Reach of Klamath River, Central Oregon, USA