Groundwater quality assessment with respect to fuzzy water quality index (FWQI): an application of expert systems in environmental monitoring

Abstract

Water quality degradation affects socio-economic development inappropriately and has dire effects on human health too. Water quality indexes (WQIs) are the methods widely used for modelling water quality status. However, using these indexes is limited by some constraints like deficiency of necessary database or uncertainty of decision-making. Throughout the ongoing research, fuzzy water quality indexes (FWQIs) were developed based on the Mamdani fuzzy inference system (FIS) to overcome the above-mentioned limitations. In other words, seven FWQIs models with different water quality parameters have been developed based on triangular and trapezoidal membership functions. Later, the developed indexes were employed to evaluate the water quality of 17 wells in Saveh Plain, Iran. Compared to the conventional WQI, the results showed that the elimination of some needed parameters in development of FWQI did not decrease the accuracy of water quality classification. However, omitting some other parameters with undesirable values made the classification of water quality unreliable. According to the results, some 35 % of wells benefitted from proper drinking water quality, while approximately 30 and 35 % of them suffered from unsuitable and very poor drinking water quality, respectively.

A comparative study of fuzzy logic-based models for groundwater quality evaluation based on irrigation indices

“…While studies have applied surface water quality criteria to groundwater quality evaluation for agricultural purposes, few studies have directly addressed groundwater quality. Similarly, while many studies have discussed the applications of FL in water resources evaluation, these have been largely limited to the assessment of water quality for potable uses [DAHIYA et al 2007;HOSSEINI--MOGHARI et al 2015;VADIATI et al 2016]. Accordingly, further research on water quality assessment, particularly in the context of groundwater use for agricultural purposes, is necessary [ALAVI et al 2010;MIRABBASI et al 2008;OSTOVARI et al 2015].…”

Assessment Framework for Natural Groundwater Contamination in Arid Regions: Development of Indices and Wells Ranking System Using Fuzzy VIKOR Method

“…Higher values of S j correspond to higher pollution with larger number of water quality parameters violating the DWQS. After calculating the water pollution indices, Equation (17) was used to estimate the water quality indices for each well. Finally, WQIo for each well field was calculated by averaging the WQIo of all the wells using Equation (18).…”

Designing a risk-based multi criteria framework for river health assessment: a case study of Taleghan basin, Iran

“…Based on Dahiya et al (2007), due to existence of different types of uncertainties in such experimental and measurement processes as sampling, sample storage, processing and analysis; employing crisp water quality indices cannot generate reliable outcomes. Hosseini-Moghari et al (2015) suggested that efficiency and accuracy of fuzzy water quality analysis in dealing with uncertainty is larger than the crisp one. Unlike such studies as Karamouz et al (2005) and Roy and Katpatal (2011), the current study used fuzzy quantifiers through OWA to reduce the uncertainty associated with water quality parameters.…”

“…A large number of studies have been undertaken regarding river quality assessment such as agricultural flow returns (Tavakoli et al, 2015), groundwater quality assessment (Hosseini-Moghari et al, 2015), reliability evaluation of municipal waste water (Varshney et al, 2013), water quality analysis of rock loose structures (Pagliara and Carnacina, 2012), soil and water quality (Srivastava et al, 2011), ecological quality assessment of lakes and reservoirs (Shah et al, 2011), etc. However, there are a few records of research work regarding determination of river quality on the basis of significant water quality parameters.…”