GIS-based comparative characterization of groundwater quality of Tabas basin using multivariate statistical techniques and computational intelligence

Abstract

Effective management of groundwater resources needs sustainable monitoring programs which are mainly performed based on water quality characterization. In the current research, hydrochemical characteristics of Tabas basin groundwater were analyzed by self-organizing map (SOM), multivariate statistical analysis and average groundwater quality index (AGWQI). Geographic information system was adopted to highlight the spatial variability of water indices, factors and clusters. AGWQI results show inappropriateness of groundwater for drinking purposes in some central and western parts of the study area (AGWQI > 100). A three-component model which explains over 80.75% of the total groundwater quality variations was suggested after factor analysis. Factor 1 (natural hydrochemical evolution of groundwater) includes high loadings of EC, TDS, TH, Ca2+ and Na+, Factor 2 (weathering and dissolution processes) includes high loadings of pH, Mg2+, HCO3− and depth, and Factor 3 (anthropogenic activities) includes high loadings of K+, Cl−, SO42− and NO3−. As the main goal of this study, groundwater data were also examined using SOM approach. Based on hydrochemical characteristics, groundwater samples were divided into three clusters. Cluster I containing 14% of groundwater samples (and sampling stations) is characterized by higher TDS, EC and TH values. Clusters II (characterized by higher Mg2+ concentration) and III (characterized by higher NO3− concentration) represent 50% and 36% of samples, respectively. Maps drawn show a meaningful compatibility among the spatial distribution of factors and clusters. This study proves that SOM can be successfully applied to characterize and classify groundwater in terms of quality on a regional scale.