Fuzzy Logic Water Quality Index and Importance of Water Quality Parameters

Abstract

Determination of status of water quality of a river or any other water sources is highly indeterminate. It is necessary to have a competent model to predict the status of water quality and to advice for type of water treatment for meeting different demands. One such model (UNIQ2007) is developed as an application software in water quality engineering. The unit operates in a fuzzy logic mode including a fuzzification engine receiving a plurality of input variables on its input and being adapted to compute membership function parameters. A processor engine connected downstream of the fuzzification unit will produce fuzzy set, based on fuzzy variable viz. DO, BOD, COD, AN, SS and pH. It has a defuzzification unit operative to translate the inference results into a discrete crisp value of WQI. The UNIQ2007 contains a first memory device connected to the fuzzification unit and containing the set of membership functions, a secondary memory device connected to the defuzzification unit and containing the set of crisp value which appear in the THEN part of the fuzzy rules and an additional memory device connected to the defuzzification unit. More advantageously, UINQ2007 is constructed with control elements having dynamic fuzzy logic properties wherein target non-linearity can be input to result in a perfect evaluation of water quality. The development of the fuzzy model with one river system is explained in this paper. Further the model has been evaluated with the data from few rivers in Malaysia, India and Thailand. This water quality assessor probe can provide better quality index or identify the status of river with 90% perfection. Presently, WQI in most of the countries is referring to physic-chemical parameters only due to great efforts needed to quantify the biological parameters. This study ensures a better method to include pathogens into WQI due to superior capabilities of fuzzy logic in dealing with non-linear, complex and uncertain systems.

Water quality index development using fuzzy logic: A case study of the Karoon River of Iran

“…Based on adequate fuzzy model results, the TDS levels in Karoon River are in the fair range. Fishes live the best in waters with a pH between 6.5 and 8.4 (Raman et al, 2009). Fish are harmed if pH becomes too acidic (falls below 4.8) or too alkaline (goes above 9.2).…”

“…Fishes live the best in waters with a pH between 6.5 and 8.4 (Raman et al, 2009). Fish are harmed if pH becomes too acidic too alkaline (goes above 9.2).…”

“…Fuzzy systems were first introduced by Zadeh (1965). Raman et al (2009) believed that fuzzy logic concepts, if used logically, could be an effective tool for some of the environmental policy matters. Jinturkar et al (2010) used the fuzzy logic for deciding the water quality index on the basis of which, water quality rankings are given to determine the quality of water.…”

A Number Description of Ghanaian Water Quality—A Case Study of the Southwestern and Coastal Rivers Systems of Ghana

“…Fishes live the best in waters with a pH between 6.5 and 8.4. Fish are harmed if pH becomes too acidic (falls below 4.8) or too alkaline (goes above 9.2) [17]. The pH of the waters during the study period ranged from 5.51 to 8.96 and was thus acceptable.…”

“…It can react with chlorine in water treatment plants to form harmful disinfectant by-products in drinking water [17]. BOD in the Southwestern and Coastal Rivers basins is dominated by naturally occurring organic material.…”