Predicting groundwater fluoride levels for drinking suitability using machine learning approaches with traditional and fuzzy logic models-based health risk assessment

Abstract

The primary objective of this study is to measure fluoride levels in groundwater samples using machine learning approaches alongside traditional and fuzzy logic models based health risk assessment in the hard rock Arjunanadi River basin, South India. Fluoride levels in the study area vary between 0.1 and 3.10 mg/L, with 32 samples exceeding the World Health Organization (WHO) standard of 1.5 mg/L. Hydrogeochemical analyses (Durov and Gibbs) clearly show that the overall water chemistry is primarily influenced by simple dissolution, mixing, and rock-water interactions, indicating that geogenic sources are the predominant contributors to fluoride in the study area. Around 446.5 km² is considered at risk. In predictive analysis, five Machine Learning (ML) models were used, with the AdaBoost model performing better than the other models, achieving 96% accuracy and 4% error rate. The Traditional Health Risk Assessment (THRA) results indicate that 65% of samples pose highly susceptible for dental fluorosis, while 12% of samples pose highly susceptible for skeletal fluorosis in young age groups. The Fuzzy Inference System (FIS) model effectively manages ambiguity and linguistic factors, which are crucial when addressing health risks linked to groundwater fluoride contamination. In this model, input variables include fluoride concentration, individual age, and ingestion rate, while output variables consist of dental caries risk, dental fluorosis, and skeletal fluorosis. The overall results indicate that increased ingestion rates and prolonged exposure to contaminated water make adults and the elderly people vulnerable to dental and skeletal fluorosis, along with very young and young age groups. This study is an essential resource for local authorities, healthcare officials, and communities, aiding in the mitigation of health risks associated with groundwater contamination and enhancing quality of life through improved water management and health risk assessment, aligning with Sustainable Development Goals (SDGs) 3 and 6, thereby contributing to a cleaner and healthier society.

Keywords

Groundwater / Fluoride / Machine learning / Health risk assessment / Fuzzy inference system / SDGs

CRediT authorship contribution statement

D. Karunanidhi: Writing - original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Project administration, Funding acquisition, Conceptualization, Writing - review & editing. M. Rhishi Hari Raj: Writing - original draft, Resources, Methodology, Investigation, Formal analysis. V.N. Prapanchan: Writing - review & editing, Validation. T. Subramani: Writing - review & editing, Formal analysis.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors are greatly indebted to the Anusandhan National Research Foundation (ANRF), New Delhi [Erstwhile, Science and Engineering Research Board (SERB)], Department of Science and Technology (DST) (Government of India) (File No.: CRG/2022/002618 Dated:22.08.2023) for providing the grant and support to carry out this work effectively.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.gsf.2025.102087.

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