{
  "abstract": "Reliable screening of agricultural water-use anomalies over large areas remains challenging because verified event labels and field observations are rarely available. We present AI4WaterGuard, a multi-source Earth Observation and artificial intelligence framework for unsupervised water-use anomaly screening across Cyprus. Using 617,862 records from 1,974 centroids across six hydrological years, six primary detector families were assessed under nested temporal cross-validation with training-consistent scaling, validity-aware scoring, and harmonised temporal support. Among the five centroid-sequence detectors, the convolutional neural network autoencoder showed the strongest response to the strong-reference controlled synthetic proxy, achieving Average Precision of $$0.893\\pm 0.130$$ and Proxy F1 of $$0.880\\pm 0.100$$ . The spatially explicit convolutional long short-term memory variational autoencoder was evaluated at finer land-pixel-sequence granularity and interpreted descriptively. Across five perturbation patterns and three severities, the convolutional neural network autoencoder achieved an equal-scenario mean Average Precision of 0.342, declining to 0.088 for weak perturbations. These synthetic metrics quantify controlled-proxy sensitivity, not field-validated detection performance. Operational post-processing on 1,157 WorldCover-eligible centroids combined threshold sensitivity, 1,000 exact-count randomisations, a primary Top-50 inspection-prioritisation queue with a nested Top-20 high-priority subset, and common-cadence spatiotemporal events. The resulting screening priorities require independent administrative and field evidence to establish physical cause or legal status.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Elena Kakoulli",
      "orcid": "0000-0003-1489-807X"
    },
    {
      "name": "Anastasios Antoniades"
    },
    {
      "name": "Stelios P. Neophytides"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cpwhc3ev2xtf7.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-76069-9",
  "fulltext_url": null,
  "id": "cpwhc3ev2xtf7",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-76069-9",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7223569148",
  "pdf_url": "https://www.nature.com/articles/s41598-026-76069-9_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/cpwhc3ev2xtf7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:34Z",
      "native_id": "10.1038/s41598-026-76069-9",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-76069-9"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "A multi-source earth observation and artificial intelligence framework for unsupervised water-use anomaly screening in Mediterranean agriculture",
  "title_text": "A multi-source earth observation and artificial intelligence framework for unsupervised water-use anomaly screening in Mediterranean agriculture",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:34Z",
  "url": "https://research.draconitic.com/paper/cpwhc3ev2xtf7",
  "venue": "Scientific Reports",
  "versions": []
}