{
  "abstract": "Urban flooding poses a critical challenge to sustainable urban development, yet long-term comparative assessments remain limited by fragmented and heterogeneous disaster records. To address this gap, this study integrates multi-source flood-event data from 20 major Chinese cities during 2000–2024 and develops an impact-based framework for assessing urban flood severity. Official statistics, news reports, and social media records are combined to construct an event-level flood database, which supports the development of a Flood Severity Index (FSI). A hybrid weighting framework that combines conventional weighting schemes with machine-learning-assisted indicator attribution is developed to improve the robustness and interpretability of severity assessment. The results reveal pronounced spatial heterogeneity: southern cities exhibit higher average severity levels and more frequent high-severity events, whereas northern cities generally show lower baseline severity but remain vulnerable to episodic extreme flood impacts. Temporal analysis indicates substantial interannual variability rather than a consistent long-term increasing or decreasing trend. These findings highlight the need for region-specific flood management strategies, including reducing persistent high-severity impacts in southern cities and improving preparedness for abrupt extreme events in northern cities. This study demonstrates the value of integrating fragmented historical records into a comparable framework for urban flood severity assessment.",
  "abstract_shown": true,
  "affiliations": [
    "Shaanxi Normal University (Xi'an, China)"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Guoqiang Peng"
    },
    {
      "affs": [
        0
      ],
      "name": "Yujing Sun"
    },
    {
      "name": "Mao Wang"
    },
    {
      "name": "Min Chen",
      "orcid": "0000-0001-8922-8789"
    },
    {
      "name": "Fengyuan Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/eh3ybk0f4ewcv.bib",
  "cited_by": 0,
  "doi": "10.5194/nhess-26-5421-2026",
  "fulltext_url": null,
  "id": "eh3ybk0f4ewcv",
  "issns": [
    "1684-9981"
  ],
  "landing_url": "https://nhess.copernicus.org/articles/26/5421/2026/",
  "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": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Copernicus GmbH",
  "ris": "https://research.draconitic.com/paper/eh3ybk0f4ewcv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:52Z",
      "native_id": "10.5194/nhess-26-5421-2026",
      "source": "crossref",
      "url": "https://nhess.copernicus.org/articles/26/5421/2026/"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "issn:1684-9981",
      "subfield": "geophysics"
    }
  ],
  "title": "Multi-data source based quantifying urban flood severity in major Chinese cities (2000–2024) using a hybrid machine-learning weighting framework",
  "title_text": "Multi-data source based quantifying urban flood severity in major Chinese cities (2000–2024) using a hybrid machine-learning weighting framework",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T00:21:11Z",
  "url": "https://research.draconitic.com/paper/eh3ybk0f4ewcv",
  "venue": "Natural Hazards and Earth System Sciences",
  "versions": [
    {
      "doi": "10.5194/egusphere-2026-2501",
      "pid": null,
      "relation": "preprint",
      "title": null,
      "venue": null
    }
  ]
}