Prioritizing Groundwater Monitoring in Data Sparse Regions using Atanassov Intuitionistic Fuzzy Sets (A-IFS)

Abstract

Water quality index (WQI) is a single measure that is commonly used to prioritize water wells and manage groundwater resources. WQI is pragmatic as it combines several water quality parameters into a single index. However, the process of aggregation is imprecise and suffers from uncertainties in measurements and subjective specification of weights. The goal of this study is to demonstrate how Atanassov’s Intuitionistic Fuzzy Sets (A-IFS) can be used to aggregate water quality parameters into a composite index to rank and prioritize groundwater wells. The A-IFS weighted geometric mean (A-IFS-WGM) method and the A-IFS based Technique for Order of Preference by Similarity to Ideal Solution (A-IFS-TOPSIS) using Euclidean (A-IFS-TOPSIS-E) and Hamming (A-IFS-TOPSIS-H) are introduced and illustrated to prioritize and rank water supply wells in a fast growing yet poorly studied area in Guntur, Andhra Pradesh, India. The concept of A-IFS entropy is also presented to directly ascertain weights from the data. This objective selection of weights from the data eliminates the subjectivity and difficulties associated with assigning relative importance to different water quality parameters. The results of the study indicate that the weights obtained using the entropy methods are consistent with the geochemical characteristics of the regional aquifer. The A-IFS-WGM method is more sensitive to weights compared to the A-IFS-TOPSIS methods which are influenced to a larger extent by the membership and non-membership values (ratings). Special consideration must be placed on ascribing the hesitation margin of the decision maker and identifying the membership values for non-preference as the methods exhibit greater sensitivity to these factors. The developed methods provide pragmatic data-driven approaches to prioritize and rank groundwater wells within a monitoring network.