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

Abstract

Determination of the status of water quality of a river or any other water source is highly indeterminate. It is necessary to have a competent model to predict the status of water quality and to show the type of water treatment that would be used to meet different demands. By exploring the behavior and limitations of conventional methods for quality evaluation, a better overall index for water quality in Iran and its application in Karoon River is proposed. Six variables are employed for the quality assessment. Numerical scales relating to the degree of quality are established for each variable to assess variations in quality and to convey findings in a comprehensive manner. 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 namely dissolved oxygen (DO), total dissolved solids (TDS), turbidity, nitrate, fecal coliform and pH. It has a defuzzification unit which operates to translate the inference results into a discrete crisp value of water quality index. The development of the fuzzy model with one river system is explained in this paper. Water quality index in most countries is only referring to physico-chemical parameters due to great efforts needed to quantify the biological parameters. This study ensures a better method to include special parameters into water quality index due to superior capabilities of fuzzy logic in dealing with non-linear, complex and uncertain systems.

Fuzzy Logic as a Tool for the Assessment of Water Quality for Reservoirs: A Regional Perspective (Lerma River Basin, Mexico)

“…The aim of this section is not to expose the full fuzzy logic theory (FL); however, it is important to give a brief introduction. FL was introduced in 1965 by [36], and it is a mathematical tool for dealing with uncertainty as it is able to measure linguistic concepts or subjective words that are fundamentally imprecise, ambiguous or fuzzy [21,37].…”

“…A membership function is a curve whose shape is defined by convenience [11,38], and that defines how each point in the antecedent is mapped to a membership value in a range of 0-1 [21]. In FIS, different shapes of membership functions can be used, such as Gaussian, bell, trapezoidal and triangular, among others.…”

“…Numerous studies have used water quality indices to determine water quality in rivers [2,3,9,[15][16][17][18][19][20]. Recently, fuzzy theory has been used to design water quality indices and also to assess the water quality in rivers [21][22][23][24][25].…”

A Mamdani Adaptive Neural Fuzzy Inference System for Improvement of Groundwater Vulnerability

“…Fuzzy logic has been surprisingly successful in many areas of geology (Demicco and Klir 2004). Some works have used fuzzy logic to assess groundwater quality (Shouyu and Guangtao 2003;Dixon 2005;Semiromi et al 2011;Kumar et al 2012;Nassery and Nejad 2012;Rezaei et al 2013;Al-Rawabdeh et al 2014;Nadiri et al 2017).…”

Water Quality Classification by Artificial Neural Network - A Case Study of Dong Nai River, Vietnam

“…It is also a numeric expression employed to transmute immense quantities of body water description data into an index, which characterizes the presenting water quality degree [8]. Available water quality indexes, however, have some limitations such as incorporating a limited number of water quality variables and providing deterministic outputs [9].…”