{
  "abstract": "Long-term flood risk assessment is vital for identifying persistent hazard-prone areas and supporting risk-informed spatial planning in rapidly urbanising regions. This study developed an interpretable geospatial framework integrating AHP, LightGBM–SHAP, and reliability-weighted game-theoretic weight fusion for the Beijing–Tianjin–Hebei (BTH) region using annual flood-related dataset for 2001–2025. Negative sample selection was refined using HAND-based low-likelihood screening and buffer-based exclusion around flood-labelled pixels. The LightGBM component showed strong internal predictive performance, with AUC, TSS, F1-score, and Kappa values of 0.92, 0.69, 0.84, and 0.69, respectively. Independent validation of the final flood risk index for 2022–2025 further confirmed its ability to identify flood-prone areas, with annual AUC values ranging from 0.63 to 0.83. High-risk areas were mainly concentrated in the central-eastern and southeastern plains, especially along the Beijing–Langfang–Tianjin–Cangzhou corridor. Flood risk fluctuated markedly and showed a weak increasing trend, with stable and increasing-risk areas accounting for 87.8% and 11.7%. Topographic factors dominated risk patterns, followed by hydrometeorological, urbanisation, and ecological factors. The framework supports long-term flood risk mapping, driver attribution, and spatial prioritisation of risk reduction measures.",
  "abstract_shown": true,
  "affiliations": [
    "Beijing City University, Beijing, China",
    "College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yuna Yan"
    },
    {
      "affs": [
        0
      ],
      "name": "Junru Jia"
    },
    {
      "affs": [
        0
      ],
      "name": "Qian Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Nina Xiong"
    },
    {
      "affs": [
        1
      ],
      "name": "Zhenyu Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiaofan Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3kj8jq9ta39ca.bib",
  "cited_by": 0,
  "doi": "10.1080/19475705.2026.2751763",
  "fulltext_url": "https://www.tandfonline.com/doi/10.1080/19475705.2026.2751763",
  "id": "3kj8jq9ta39ca",
  "issns": [
    "1947-5713",
    "1947-5705"
  ],
  "landing_url": "https://doi.org/10.1080/19475705.2026.2751763",
  "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"
    ],
    [
      "lcc",
      "HD61"
    ],
    [
      "lcc",
      "TD1-1066"
    ],
    [
      "openalex",
      "2306|Flood Risk Assessment and Management"
    ]
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  "openalex": "W7223403985",
  "pdf_url": "https://www.tandfonline.com/doi/pdf/10.1080/19475705.2026.2751763?needAccess=true",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Taylor & Francis Group",
  "ris": "https://research.draconitic.com/paper/3kj8jq9ta39ca.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:04Z",
      "native_id": "26addcf25cc8485c8a23a6909226e174",
      "source": "doaj",
      "url": "https://doi.org/10.1080/19475705.2026.2751763"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2306|Flood Risk Assessment and Management",
      "subfield": "climate"
    }
  ],
  "title": "Geospatially interpretable long-term flood risk assessment using integrated weighting and LightGBM–SHAP",
  "title_text": "Geospatially interpretable long-term flood risk assessment using integrated weighting and LightGBM–SHAP",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:04Z",
  "url": "https://research.draconitic.com/paper/3kj8jq9ta39ca",
  "venue": "Geomatics, Natural Hazards & Risk",
  "versions": []
}