{
  "abstract": "Heavy metal (HM) accumulation in lake sediments poses ecological risks and may increase aquatic organism vulnerability to disease, requiring cost-effective monitoring tools. This study developed an interpretable multi-output machine learning (ML) framework to predict sediment HMs (As, Cd, Cu, Hg, Ni, Pb, and Zn) in Swan Lake, using field variables (pH, dissolved oxygen, and temperature), sediment texture (median grain size), and aluminum content (Al%). Models with nested preprocessing and cross-validation achieved strong performance (R 2 = 0.824–0.997). Optimal algorithms varied by metal, while random forest showed the most consistent results, balancing accuracy, low error, and robustness to outliers. Predicted concentrations were integrated with ecological risk indices (Igeo, PLI, and Hakanson RI), indicating low pollution with localized Zn hotspots. Explainability analysis identified pH and Al% as key drivers, supporting ML-based screening for aquatic monitoring and early warning systems.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Fisheries, Sylhet Agricultural University, Sylhet 3100, Bangladesh",
    "Center of Excellence in Sustainable Disaster Management, School of Engineering and Technology, Walailak University, Nakhon Si Thammarat 80161, Thailand",
    "Department of Environmental Science, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh",
    "Department of Agricultural Construction and Environmental Engineering, Sylhet Agricultural University, Sylhet 3100, Bangladesh",
    "Department of Agricultural and Biological Engineering, Mississippi State University, 130 Creelman Street, Mississippi State, MS 39762, USA",
    "Faculty of Agricultural Economics and Business Studies, Sylhet Agricultural University, Sylhet 3100, Bangladesh",
    "Department of Computer Science, UiT The Arctic University of Norway, 6050, Langnes, 9037 Tromsø, Norway",
    "Department of Computational and Applied Mechanics, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil",
    "Department of Fish Health Management, Laboratory of Fish Diseases Diagnosis and Pharmacology, Faculty of Fisheries, Sylhet Agricultural University, Sylhet, Bangladesh",
    "Faculty of Veterinary Medicine, Universitas Brawijaya, Malang, East Java, 65151, Indonesia",
    "Experimental Zooprophylactic Institute of Piedmont, Liguria and Aosta Valley, Via Bologna 148, 10154 Turin, Italy"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Md. Abdullah Al Mamun Hridoy",
      "orcid": "0009-0003-8465-625X"
    },
    {
      "affs": [
        1
      ],
      "name": "Pakorn Ditthakit",
      "orcid": "0000-0001-9847-2177"
    },
    {
      "affs": [
        2
      ],
      "name": "Akib Hosen"
    },
    {
      "affs": [
        3
      ],
      "name": "Mohd. Saifur Rahman"
    },
    {
      "affs": [
        4
      ],
      "name": "Minhaj Uddin"
    },
    {
      "affs": [
        5
      ],
      "name": "Kamrul Hasan"
    },
    {
      "affs": [
        3
      ],
      "name": "Yeaman Hossain"
    },
    {
      "affs": [
        6
      ],
      "name": "Chiara Bordin"
    },
    {
      "affs": [
        7
      ],
      "name": "Leonardo Goliatt",
      "orcid": "0000-0002-2844-9470"
    },
    {
      "affs": [
        8,
        9
      ],
      "name": "Md. Abdullah Al Mamun"
    },
    {
      "affs": [
        10
      ],
      "name": "Paolo Pastorino",
      "orcid": "0000-0002-0585-1168"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/05qyvey8p2k2r.bib",
  "cited_by": 5,
  "doi": "10.1016/j.isci.2026.116549",
  "fulltext_url": null,
  "id": "05qyvey8p2k2r",
  "issns": [
    "2589-0042"
  ],
  "landing_url": "https://doi.org/10.1016/j.isci.2026.116549",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2310|Heavy metals in environment"
    ]
  ],
  "openalex": "W7167577740",
  "pdf_url": null,
  "pmcid": "PMC13378350",
  "pmid": "42491591",
  "published": "2026-07-01",
  "publisher": "Cell Press",
  "ris": "https://research.draconitic.com/paper/05qyvey8p2k2r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:59Z",
      "native_id": "W7167577740",
      "source": "openalex",
      "url": "https://doi.org/10.1016/j.isci.2026.116549"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2310|Heavy metals in environment",
      "subfield": "environmental-science"
    }
  ],
  "title": "Machine learning-driven prediction of heavy metal pollution for aquatic health surveillance and disease control",
  "title_text": "Machine learning-driven prediction of heavy metal pollution for aquatic health surveillance and disease control",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T13:21:03Z",
  "url": "https://research.draconitic.com/paper/05qyvey8p2k2r",
  "venue": "iScience",
  "versions": []
}