Evaluation of correlation of physicochemical parameters and major ions present in groundwater of Raipur using discretization

Abstract

Groundwater, vital for human consumption and agriculture, ecosystem support, and industrial activities, requires sustainable management using proper quality assessment techniques. This study examines the relationship between physicochemical parameters and major ions in groundwater samples collected from 44 regions in Raipur, using sensor-based data acquisition alongside traditional methods. Employing K-means clustering for data discretization, correlations between parameters are highlighted. Results show positive associations among EC, TDS, TH, and TA. ArcGIS interpolation maps visualize spatial distribution. Addressing class imbalance, an upsampling technique is utilized. Machine learning algorithms, including Logistic Regression and Random Forest, classify water quality with accuracies of 98.8 % and 98.3 %, respectively. This research, blending traditional and sensor-based methods, emphasizes informed water management.