Comparison between fuzzy logic and water quality index (CCME) methods: A case of water quality assessment for livestock watering

Abstract

This study aimed to evaluate groundwater quality for a group of wells in the area located between Sinjar and Tal Afar districts, west of Nineveh Governorate, to know the most appropriate places to water livestock. Samples were collected from twenty wells distributed throughout the study area and were measured for six months. A fuzzy logic model was developed to integrate eight parameters: pH, Ca, Mg, Na, Cl, PO4, SO4, and Fecal coliform (F.Coli). Membership functions for a fuzzy logic model of groundwater quality for livestock watering (GQLW) were constructed using linguistic expressions and trapezoidal shapes. The model was used on a data set of chemical analyses, and biological groundwater samples were taken from the study area. GQLW values ranged from fair to poor. This is due to the high concentrations of most studied parameters, which exceeded the permissible limits for livestock watering in groundwater in the southern part of the study area. The spatial distribution maps of the study area also matched the results of fuzzy Logic, which explains that the best groundwater quality is found in the northern regions. GQLW model evaluation makes this approach a more reliable way to evaluate water quality than traditional methods for assessing groundwater quality data; the correlation coefficient between the acquired data and the CCME quality index had to be estimated. The results of this new indicator showed a respectable correlation (0.76). The GQLW can be a useful tool for decision-making regarding groundwater management in the study area.