Explainable Machine Learning Frameworks for Groundwater Quality Classification: A Review of CatBoost and SHAP-Based Approaches

Groundwater is a vital source of freshwater for the entire world and the main support to drinking water,agricultural and livestock use, especially in areas with little rain or in droughts. Groundwater quality has sufferedconsiderably in many areas mainly due to the fast growth of the population, large scale agriculture, industrial activities, andclimate change, thereby posing very serious risks to both the environment and people. Traditional methods for groundwaterquality assessment techniques which are mainly based on physicochemical analysis, threshold standards, and water qualityindices (WQIs), fail to capture nonlinear relationships among hydrochemical parameters and have limited predictive anddecision-making capabilities. Machine learning (ML) techniques have been recognized as powerful alternatives forgroundwater quality modeling by processing high-dimensional data, revealing hidden patterns, and improving the predictiveaccuracy. Nonetheless, theinterpretability issues associated with most ML-based groundwater studies make it hard forpractitioners to trust and accept them in the management and policy contexts of the real world. The review is a comprehensivesynthesis of the latest developments in the use of machine learning for groundwater quality classification, with a specialemphasis on the ensemble-based methods and explainable artificial intelligence (XAI) techniques. The focus is on theinterpretable frameworks that combine CatBoost classifiers and SHapley Additive exPlanations (SHAP) for producingaccurate, robust, and transparent assessments of groundwater quality. The influential methodologies, the datasets used, theevaluation strategies, the interpretability techniques, and the research gaps are critically scrutinized to direct the future stepsin the sustainable management of groundwater quality.