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Determination of conditioning factors for mapping nickel contamination susceptibility in groundwater in Kanchanaburi Province, Thailand, using random forest and maximum entropy

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Abstract

Groundwater pollution from nickel (Ni) has been a severe concern in Kanchanaburi Province, Thailand. Recent assessments revealed that the Ni concentration in groundwater, particularly in urban areas, often exceeded the permissible limit. The challenge for groundwater agencies is therefore to delineate regions with high susceptibility to Ni contamination. In this study, a novel modeling approach was applied to a dataset of 117 groundwater samples collected from Kanchanaburi Province between April and July 2021. Twenty site-specific initial variables were considered as influencing factors to Ni contamination. The Random Forest (RF) algorithm with Recursive Feature Elimination (RFE) function was used to select the fourteen most influencing variables. These variables were then used as input features to train a ME model to delineate the Ni contamination susceptibility at a high confidence (Area Under the Curve (AUC) validation value of 0.845). Ten input variables of the altitude, geology, land use, slope, soil type, distance to industrial areas, distance to mining areas, electric conductivity, oxidation–reduction potential, and groundwater depth were discovered in the most explaining the variation of spatial Ni contamination at very high (95.47 km2) and high (86.65 km2) susceptibility. This study devises the novel machine learning approach to identify the conditioning factors and map Ni contamination susceptibility in the groundwater, which provides a baseline dataset and reliable methods for the development of a sustainable groundwater management strategy.

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Acknowledgements

The researchers would like to thank the Interdisciplinary Program in Environmental Science, Graduate School, Chulalongkorn University, Centre for Agriculture and the Bioeconomy, Queensland University of Technology, and Hue University of Agriculture and Forestry, Hue University. We acknowledge financial support from the National Research Council of Thailand (NRCT): NRCT5-RSA63001-06 and partially support by the Ratchadapisek Sompoch Endowment Fund (2022), Chulalongkorn University (765007-RES02). Nguyen Ngoc Thanh has received the ASEAN/NON-ASEAN Scholarship and the 90th Anniversary of Chulalongkorn University Scholarship from Chulalongkorn University for Ph.D. Program at Graduate School, Chulalongkorn University. We are grateful for the thorough reviews of anonymous reviewers. Their valuable comments significantly improved the earlier draft of this article.

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Authors and Affiliations

  1. Interdisciplinary Program in Environmental Science, Graduate School, Chulalongkorn University, Bangkok, 10330, Thailand

    Nguyen Ngoc Thanh

  2. Department of Geology, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand

    Srilert Chotpantarat

  3. Center of Excellence in Environmental Innovation and Management of Metals (EnvIMM), Environmental Research Institute, Chulalongkorn University (ERIC), Bangkok, 10330, Thailand

    Srilert Chotpantarat

  4. University of Agriculture and Forestry, Hue University, 102 Phung Hung Str, Hue City, Thua Thien Hue, 53000, Vietnam

    Nguyen Ngoc Thanh & Nam-Thang Ha

  5. Centre for Agriculture and the Bioeconomy, Queensland University of Technology, 2 George St, Brisbane, QLD, 4000, Australia

    Nguyen H. Trung

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Contributions

“Conceptualization, N.N.T. and S.C..; methodology, N.N.T. and S.C.; validation, N.N.T. and S.C.; formal analysis, N.N.T.; investigation, N.N.T.and S.C.; resources, S.C.; data curation, N.N.T. and S.C.; writing—original draft preparation, N.N.T.; writing—review and editing, S.C., H.N.T., N.H.T.; visualization, N.N.T.; supervision, S.C.; project administration, S.C.; funding acquisition, S.C. All authors reviewed the manuscript.”

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Thanh, N.N., Chotpantarat, S., Ha, NT. et al. Determination of conditioning factors for mapping nickel contamination susceptibility in groundwater in Kanchanaburi Province, Thailand, using random forest and maximum entropy. Environ Geochem Health 45, 4583–4602 (2023). https://doi.org/10.1007/s10653-023-01512-z

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