Assessing hydrogeochemical facies and Groundwater Quality Index in rapidly urbanizing coastal region: a GIS-based approach with machine learning for enhanced management.

Abstract

Groundwater is an essential freshwater source worldwide, but increasing pollution poses risks to its sustainability. This study applied a comprehensive approach to assess hydrogeochemical facies and groundwater quality in Odisha's large low-lying coastal regions. Analysis of 136 samples revealed that sodium (9.4%), potassium (40.8%), bicarbonate (2.1%), and chloride (2.1%) exceeded WHO limits. The Groundwater Quality Index (GQI) map classified 5.1% of samples as "excellent," 39.4% as "good," 31.3% as "poor," 13.8% as "very poor," and 10.2% as "unsuitable" for use. Additionally, the GQI values demonstrate a random spatial autocorrelation (- 0.06) likely due to diverse influences. The study identified the expansion of agricultural (43%) and built-up areas (13%) from the Land Use/Land Cover (LULC) map. Piper diagram and Gibbs plots suggest continued freshening, rock-water interaction, and seawater intrusion. Groundwater levels fall between 0 to 2 m below ground level (mbgl), primarily due to excessive groundwater extraction. The Sodium (Na⁺) vs. Chloride (Cl^-) cross plot shows most samples align with the mixing line, with some deviations indicating multiple contamination sources. The strong correlation (> 0.90) between total dissolved salts (TDS), electrical conductivity (EC), Na⁺, and Cl^- signals seawater intrusion, highlighting the complex interaction between human activities and natural processes. The proposed machine learning (ML) models like random forest (RF), artificial neural network (ANN), decision tree, and linear regression (LR) offer a reliable alternative to traditional GQI methods, addressing the challenges of extensive sampling and data management. Among these, RF exhibited the highest predictive accuracy (coefficient of correlation (R²) = 95%), surpassing ANN (R² = 82%), decision tree (R² = 81%), and LR (R² = 67%) as the most effective model for GQI prediction. Potassium (K⁺) stands out as a key indicator of contamination. GQI, LULC map, and ML methods improve understanding of contamination sources and support systematic groundwater management.