Appukuttan Ajayakumar, Aju C D, Reghunath Rajesh, Srinivas Reji, Krishnan K Anoop, S Arya
Department of Geology, University of Kerala, Trivandrum, India.
Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India, 411008.
Water Res. 2025 Sep 15;284:123884. doi: 10.1016/j.watres.2025.123884. Epub 2025 May 21.
Groundwater quality assessment is essential for ensuring sustainable water resource management, particularly in regions heavily dependent on groundwater for domestic and agricultural needs. This study aims to investigate the hydrochemical characteristics and assess the drinking water quality of groundwater in the Ithikkara River Basin, a tropical region in southern India, by integrating hydrogeochemical methods with unsupervised learning and explainable artificial intelligence (XAI). A total of 111 groundwater samples were analysed for major ions, hydrochemical facies, and water quality indices. Self-Organizing Maps (SOM) identified three distinct groundwater clusters, each exhibiting unique geochemical signatures. Hydrochemical facies analysis revealed dominant Na⁺-Cl⁻ and mixed Ca²⁺-Na⁺-HCO₃⁻ types, influenced by silicate weathering, cation exchange, and anthropogenic activities. The Entropy Water Quality Index (EWQI) showed that 89.4 % of samples were of excellent to good quality, with moderate-quality zones located near agricultural and industrial areas. A Random Forest-based ensemble model achieved high predictive accuracy (R² = 0.871), and SHAP analysis revealed Na⁺, K⁺, and TDS as the primary contributors to water quality degradation. The integration of SOM clustering with interpretable machine learning offers a powerful framework for understanding groundwater evolution and guiding sustainable water management in tropical river basins.
地下水质量评估对于确保水资源的可持续管理至关重要，特别是在严重依赖地下水满足生活和农业需求的地区。本研究旨在通过将水文地球化学方法与无监督学习和可解释人工智能（XAI）相结合，调查印度南部热带地区伊蒂卡拉河流域地下水的水化学特征并评估其饮用水质量。共分析了111个地下水样本的主要离子、水化学相和水质指标。自组织映射（SOM）识别出三个不同的地下水簇，每个簇都呈现出独特的地球化学特征。水化学相分析表明，受硅酸盐风化、阳离子交换和人为活动影响，主要为Na⁺-Cl⁻型和混合Ca²⁺-Na⁺-HCO₃⁻型。熵水质指数（EWQI）显示，89.4%的样本水质优良，中等质量区域位于农业和工业区附近。基于随机森林的集成模型具有较高的预测准确性（R² = 0.871），SHAP分析表明Na⁺、K⁺和总溶解固体（TDS）是水质恶化的主要因素。SOM聚类与可解释机器学习的结合为理解热带河流域地下水演化和指导可持续水资源管理提供了一个强大的框架。
