Comparison between self‐organizing map and principal component analysis for water quality assessment and hydro‐geochemical characterization in dyke intruded complex geological settings

Abstract

AbstractHydro‐geochemical characterization is challenging in dyke intruded complex geological setting. The comparison between self‐organizing map (SOM) classification and principal component analysis (PCA) is used for better understanding of hydrogeological process surrounding Amarpur dyke in Dhanbad district, Jharkhand. Total 30 water samples were collected and tested for 12 physicochemical parameters. The K‐means clustering with SOM grouped the water quality data into cluster 1 (46.67%, low mineralization), cluster 2 (36.67%, moderate mineralization) and cluster 3 (16.67%, high mineralization). The clusters of the majority of samples identified by PCA analysis is almost same as identified by SOM with little difficulty in discriminating between cluster 2 and cluster 3. The transformation of Ca‐HCO3 to Ca‐Cl‐SO4 occurred because of exchange of Ca2+ with Na+ adsorbed in the aquifer leading excess of sulphate ions. The results of this study suggest that SOM is an effective tool for a better understanding of patterns and processes driving water quality.