{
  "abstract": "Background Ensuring sustainable water management (SDG 6) requires reliable IoT-based sensor networks, yet low-cost deployments are often hindered by environmental noise and the opaque nature of complex AI models. This research aims to develop a robust contaminant classification framework that prioritizes actionable transparency and physical consistency in environmental monitoring. Methods Time-series water quality data were analyzed using Random Forest (RF), LSTM, and Transformer models. Beyond standard performance metrics (Weighted F1-Score), this study introduces a Dual-Layer Validation (DLV) framework powered by SHAP-based Explainable AI (XAI) to verify the models’ alignment with hydrogeochemical principles. Results The RF model achieved the highest performance (F1 = 0.872), outperforming LSTM (0.852) and Transformer (0.817) (p < 0.05). Crucially, the XAI analysis revealed a strong cross-architecture consensus: both ensemble and attention-based models independently identified Turbidity and pH as the principal eco-chemical drivers of contamination. Conclusion The DLV framework demonstrates that transparent models can effectively learn genuine physical and hydrogeochemical fingerprints rather than statistical artifacts. This cross-validated resilience to sensor noise offers a critical pathway toward sustainable, trustworthy, and cost-effective IoT water quality management systems.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Informatics Engineering, Universitas Muria Kudus"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Wibowo Harry Sugiharto"
    },
    {
      "affs": [
        0
      ],
      "name": "Rizkysari Meimaharani"
    },
    {
      "affs": [
        0
      ],
      "name": "Ahmad Abdul Chamid"
    },
    {
      "affs": [
        0
      ],
      "name": "Muhammad Imam Ghozali"
    },
    {
      "affs": [
        0
      ],
      "name": "Alif Catur Murti"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/eb52t3tjre3qe.bib",
  "cited_by": 0,
  "doi": "10.1007/s43621-026-04210-9",
  "fulltext_url": "https://doi.org/10.1007/s43621-026-04210-9",
  "id": "eb52t3tjre3qe",
  "issns": [
    "2662-9984"
  ],
  "landing_url": "https://doi.org/10.1007/s43621-026-04210-9",
  "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": [
    [
      "lcc",
      "GE1-350"
    ],
    [
      "openalex",
      "2312|Water Quality Monitoring Technologies"
    ]
  ],
  "openalex": "W7171072656",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Springer",
  "ris": "https://research.draconitic.com/paper/eb52t3tjre3qe.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:33Z",
      "native_id": "39c5588a32a24a9ba0f6c682dbfbe4fa",
      "source": "doaj",
      "url": "https://doi.org/10.1007/s43621-026-04210-9"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2312|Water Quality Monitoring Technologies",
      "subfield": "hydrology-soils"
    }
  ],
  "title": "Explainable AI with dual-layer validation framework for ensuring noise resilience and physical consistency in low-cost sustainable water quality monitoring",
  "title_text": "Explainable AI with dual-layer validation framework for ensuring noise resilience and physical consistency in low-cost sustainable water quality monitoring",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:33Z",
  "url": "https://research.draconitic.com/paper/eb52t3tjre3qe",
  "venue": "Discover Sustainability",
  "versions": []
}