Geochemical and machine learning approaches to groundwater fluoride prediction in Karaga District, Northern Ghana.

Fluoride contamination of groundwater affects over 200 million people globally, with Africa serving as a primary hotspot. The Karaga District in Ghana’s Northern Region represents a critical fluoride hotspot, where 4 out of 10 children likely face exposure to concentrations exceeding 1.5 mg/L. Despite being identified as high-risk, the specific geochemical mechanisms controlling fluoride mobilization in the district’s Voltaian Supergroup aquifers remain inadequately understood, limiting the development of targeted mitigation strategies. This study aimed to develop and validate an integrated framework combining geochemical modelling, compositional data analysis, and machine learning to predict fluoride concentrations and elucidate mobilization mechanisms in Karaga District’s groundwater. About 34 groundwater samples from the Karaga District were collected and analyzed for hydrochemical parameters. The data was processed using PHREEQC for geochemical modelling and isometric log-ratio transformation for compositional analysis. Additionally, 6 supervised machine learning algorithms were trained on 152 archived samples from neighbouring districts and subsequently validated using the 34 newly collected groundwater samples. A mechanistic Mobility Index was developed using fluoride-independent components and entropy-based weighting. Fluoride concentrations ranged from 0.07 to 6.04 mg/L, with 17.6% exceeding WHO guidelines. Na-HCO3 waters dominated (64.7%), but Na-Cl waters exhibited the highest fluoride (mean 3.75 mg/L), revealing that evaporite dissolution drives extreme contamination. Machine learning identified total dissolved solids and pH as primary predictors, demonstrating nonlinear fluoride behaviour. The Multilayer Perceptron model achieved R2 of 0.668, while the Mobility Index demonstrated exceptional discrimination for WHO exceedance (AUROC 0.94), with robust spatial transferability across communities. This integrated approach provides a mechanistically grounded, field-deployable framework for fluoride risk assessment. The Mobility Index enables cost-effective community screening using only basic measurements, supporting targeted intervention strategies in fluoride-endemic regions globally.