275 Advancing the Understanding and Management of Groundwater Pollution with Arsenic and Other Geogenic Contaminants Using Geospatial Tools, Machine Learning, and Data Science, Part II
The growing trend of data aggregation and analysis, along with emerging technologies like machine learning, has significantly improved our understanding of groundwater basins, enabling better assessments of water quality for direct consumption and treatment in groundwater supply management.
Wednesday, 22 October 2025
1:30 PM - 5:30 PM
HBGCC, 210AB
How to catch multi-pressure, multi-scale, multi-layered aquifer processes? A multi-tracer and multi-clustering strategy in Kelantan, Malaysia
Paper number: 275-11
Program: Technical Sessions
Time: 4:05 PM – 4:20 PM
Authors
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Seyed Reza SaghravaniMarine Chemistry and Biochemistry Department, Institute of Oceanology Polish Academy of Sciences, Powstańców Warszawy 55, 81-712 Sopot
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Ismail YusoffDepartment of Geology, University of Malaya, 50603 Kuala Lumpur, Selangor
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Guillaume BertrandUniversity of Bourgogne Franche-Comté, UMR UFC CNRS 6249 Chrono-Environment, 16 route de Gray 25000 Besançon, 4 Place Tharradin, 25200 Montbéliard
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Yatimah AliasDepartment of Chemistry, University of Malaya, 50603 Kuala Lumpur, Selangor
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Prosun Bhattacharya Presenting AuthorKTH-International Groundwater Arsenic Research Group, Department of Sustainable Development, Environmental Science and Engineering, KTH Royal Institute of Technology, SE-100 44 Stockholm
Abstract
The Kelantan River Catchment in northeast Malaysia was selected as a representative study area due to its complex hydrogeological setting in tropical coastal aquifers, characterized by the interaction of inland recharge, seawater intrusion, and anthropogenic contamination. To unravel the underlying mixing processes, five clustering algorithms, namely Hierarchical Cluster Analysis, K-Means, Fuzzy C-Means, Genetic K-Means, and Self-Organizing Maps—were applied to an integrated dataset comprising major ions, trace elements (F⁻, Br⁻), nutrients (NO₃⁻, NO₂⁻), and environmental isotopes (δ¹⁸O, δ²H, δ¹³C, δ¹⁵N, and ³H). The clustering results distinguished groundwater facies by residence time, source origin, and contaminant signatures. Shallow recharge zones exhibited depleted δ¹⁸O and δ²H, low TDS, and high ³H, consistent with recent infiltration and low rock interaction. In contrast, deeper saline zones showed enriched δ¹³C and δ¹⁵N, low ³H, and elevated Na⁺ and Cl⁻, indicating older water affected by water–rock interaction and seawater mixing. High NO₃⁻+NO₂⁻ concentrations, mainly captured in a single cluster in the shallow aquifer, reveal localized contamination likely driven by fertilizer application. The integration of isotopic and clustering frameworks revealed vertical and lateral heterogeneity in aquifer layers and enhanced the interpretability of geogenic and anthropogenic sources. These findings demonstrate how the utility of integrating isotopes, water chemistry data, and unsupervised clustering techniques can expand traditional hydrogeochemical analysis and support improved groundwater quality assessment and management to characterize mixing, recharge, and contamination processes in a tropical coastal aquifer system.
Geological Society of America Abstracts with Programs. Vol. 57, No. 6
doi: 10.1130/abs/2025AM-412532
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