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Prediction of fluoride contamination in groundwater of Nigeria using a weighted ensemble machine-learning framework.
| Environmental Geochemistry and Health | Usman US, Cheng J, Yan B, Rimven NB, Yousif MH, Garba AT, Indrick AA. | 48(13):527.
Children Groundwater Infants Nigeria Other

Abstract

Original abstract online at
https://link.springer.com/article/10.1007/s10653-026-03362-x

Fluoride (F–) contamination of groundwater remains a significant health risk in Nigeria, affecting rural and urban communities. This study examines the spatial distribution and factors influencing F– levels using a weighted ensemble machine learning model to forecast high-risk areas and estimate exposed populations. The primary factor controlling geogenic F– is the aquifer lithology, with the highest F– levels found in groundwater from crystalline basement complexes and sedimentary sandstones. The problem is exacerbated in dry regions, including semiarid and arid zones, where evaporation rates increase, concentrating solutes in the water. Fluoride levels exceed World Health Organization (WHO) standards at two primary geological locations: the sedimentary aquifers of the Sokoto and Middle Niger Basins, and the Precambrian crystalline basement aquifers across northern Nigeria. Land-use and land-cover conditions (croplands, urban areas, and bare soils) also significantly affect groundwater hydrochemistry. Anthropogenic inputs (agriculture and urbanization) may contribute to localized F– mobilization. Feature importance analysis identified well depth as the most influential predictor (20.2%), followed by HCO3– (16.6%), Cl– (13.8%), total cations (13.3%), and total anions (12.7%), confirming the dominance of hydrogeological and geochemical controls. The weighted ensemble model, combining XGBoost, Random Forest, Extra Trees, and Histogram-based Gradient Boosting, achieved an accuracy of 83.74% with a Gini index of 0.71 (AUC-ROC = 0.85). Among individual base learners, Extra Trees achieved the highest Gini index (0.72). The model demonstrated high specificity (89.16%) and negative predictive value (87.06%), supporting its usefulness for identifying likely safe groundwater sources. Although Extra Trees achieved the highest individual Gini index, the weighted ensemble provided balanced overall performance across multiple evaluation metrics. The analysis reveals that more than 19,443 individuals, including 9951 infants and 7795 children, are at risk, particularly in the northern regions of Nigeria. The study highlights public health concerns, including the risk of dental and skeletal fluorosis, among communities relying on untreated groundwater. Both anthropogenic and geogenic processes jointly govern F– distribution. However, the resolution and geographic density of the input data limit its accuracy. We recommend using sophisticated methods and higher-resolution geochemical data to improve risk estimates in Nigeria and other comparable African regions.

Data availability

No datasets were generated or analysed during the current study.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 2595 kb) (download DOCX )

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Acknowledgement

The authors gratefully acknowledge the help of editors and reviewers in the future.

Funding

This work was financially supported by the Hubei International Sciences and Technology Cooperation Project (No. 2024EHA041), the National Key Research and Development Program of China (No.2022YFC3703705), the deep-time Digital Earth (DDE) Big Sciences Program, and the National Natural Science Foundation of China (No. U1911205).

Author information

Authors and Affiliations

  1. School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China

    Usman Sunusi Usman, Jianmei Cheng, Bing Yan, Mohamed Hussein Yousif & Abara ABiabak Indrick

  2. State Key Laboratory of Deep Geothermal Resources, Wuhan, 430074, China

    Jianmei Cheng

  3. Kano University of Science and Technology, Wudil, Nigeria

    Usman Sunusi Usman

  4. School of Earth Resources, China University of Geosciences, Wuhan, China

    Abdulrahman Taiye Garba

  5. Nigerian Geological Survey Agency (NGSA), Abuja, Nigeria

    Namsak Bitrus Rimven

Contributions

Conceptualization, Usman Sunusi Usman, and Jianmei Cheng; methodology, Usman Sunusi Usman; software, Usman Sunusi Usman, and Mohamed Hussein Yousif; validation, Bing Yan, Abara A Biabak Indrick, and Jianmei Cheng; formal analysis, Usman Sunusi Usman and Abdulrahman Taiye Garba; investigation, Usman Sunusi Usman; resources, Namsak Bitrus Rimven; data curation, Usman Sunusi Usman and Mohamed Hussein Yousif; writing-original draft preparation, Usman Sunusi Usman; writing-review and editing, Usman Sunusi Usman and Jianmei Cheng; visualization, Usman Sunusi Usman; supervision, Jianmei Cheng. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Jianmei Cheng.

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Conflict of interest

The authors declare no conflict of interest.

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Not applicable.

Consent to participate

All authors reviewed and approved the final manuscript.