Flood resilience in cities and urban agglomerations: a systematic review of hazard causes, assessment frameworks, and recovery strategies based on LLM tools

With the increasing frequency of extreme weather events, the study of flood resilience in cities and urban agglomerations has become a critical topic for addressing climate change. This paper systematically reviews three key research directions—flood causes, assessment frameworks, and resilience enhancement strategies—by integrating quantitative and qualitative approaches across 412 relevant studies. The study innovatively employs large language models (LLMs) and machine learning techniques to achieve a structured research synthesis. The study identifies four major issues in current research on flood resilience in cities and urban agglomerations: (1) Existing studies predominantly focus on static indicators for individual cities, neglecting the dynamic interactions within urban agglomeration systems, making it challenging to reveal the real characteristics of cross-regional resource allocation and disaster propagation. (2) Governance mechanisms in urban agglomerations lack operational feasibility; conflicting interests among cities often reduce regional synergy to mere formalities. (3) There is an overemphasis on real-time risk monitoring and assessment models, while the practical value of disaster prediction models and pre-disaster planning is often overlooked. While existing studies consider inequities in regional and population vulnerabilities, they lack focus on integrating these needs into resilience frameworks and their practical implementation. Based on these findings, this study recommends incorporating the compounding effects of multidimensional factors, enhancing the implementability of regional collaborative governance and pre-disaster planning, and embedding economic practicality and universal applicability into the design of resilience assessment and enhancement frameworks.