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<p>Compound Floods (CompF), driven by interactions among coastal, fluvial, and pluvial drivers, pose heightened risks to coastal cities under climate change (CC) and rapid urbanization. This study systematically reviews the causes, mechanisms, and assessment frameworks of CompF, with a particular focus on the role of AI in enhancing flood prediction and risk management. Following PRISMA guidelines, a systematic literature review was conducted using Web of Science and Scopus databases, covering 2015–2025. A total of 898 unique articles were analyzed through bibliometric, scientometric, and thematic analyses. Keyword trends, co-authorship, and country-level contributions were analyzed using VOSviewer, while thematic synthesis was employed to identify recurring patterns, gaps, and emerging research themes. These themes centered on the definitions, causes, mechanisms, forms, and models used to assess compound flooding in coastal cities. The impact of CC and the future projections are also documented. Finally, a conceptual framework is developed to integrate flood typology, drivers, assessment models, and CC projections. The review highlights a growing scholarly attention to CompF. Physical and statistical models remain dominant, while AI-driven and hybrid approaches are emerging, with climate change projections indicating more frequent and severe compound flood events. This study provides policymakers, planners, and scientists with an integrated framework for improved prediction, mitigation, and resilience strategies. It emphasizes the value of AI-based approaches and interdisciplinary collaboration in addressing future CompF risks in coastal cities.</p>

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Compound flooding risks in coastal cities: a review of causes, mechanisms, and assessment methodologies

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月5日

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  • Elahe Jahani,
  • Fahim Ullah,
  • Amirhossein Heravi

摘要

Compound Floods (CompF), driven by interactions among coastal, fluvial, and pluvial drivers, pose heightened risks to coastal cities under climate change (CC) and rapid urbanization. This study systematically reviews the causes, mechanisms, and assessment frameworks of CompF, with a particular focus on the role of AI in enhancing flood prediction and risk management. Following PRISMA guidelines, a systematic literature review was conducted using Web of Science and Scopus databases, covering 2015–2025. A total of 898 unique articles were analyzed through bibliometric, scientometric, and thematic analyses. Keyword trends, co-authorship, and country-level contributions were analyzed using VOSviewer, while thematic synthesis was employed to identify recurring patterns, gaps, and emerging research themes. These themes centered on the definitions, causes, mechanisms, forms, and models used to assess compound flooding in coastal cities. The impact of CC and the future projections are also documented. Finally, a conceptual framework is developed to integrate flood typology, drivers, assessment models, and CC projections. The review highlights a growing scholarly attention to CompF. Physical and statistical models remain dominant, while AI-driven and hybrid approaches are emerging, with climate change projections indicating more frequent and severe compound flood events. This study provides policymakers, planners, and scientists with an integrated framework for improved prediction, mitigation, and resilience strategies. It emphasizes the value of AI-based approaches and interdisciplinary collaboration in addressing future CompF risks in coastal cities.