城市洪涝模型研究热点演化及AI大模型应用展望

Evolution of Research Hotspots in Urban Flood Modelling and Prospects for Applications of Large AI Models

摘要:

城市洪涝灾害频发对城市安全运行构成严峻挑战，而城市洪涝模型的精准高效模拟则是灾害防控的关键支撑。基于Web of Science核心合集数据库2000—2026年的文献数据开展知识图谱分析，系统梳理城市洪涝模型研究的热点演化与发展趋势。结果表明，该领域经历了从水文水动力机理模型主导，到机器学习、深度学习等数据驱动方法快速融入的演变过程。但AI大模型相关方法尚未形成独立聚类，其在城市洪涝领域的研究尚处探索阶段。前瞻性地探讨了AI大模型的应用前景，并指出有待结合具体场景验证深化。研究旨在为城市洪涝模型与AI大模型的交叉研究提供参考。

Abstract:

The frequent occurrence of urban flood disasters pose a serious challenge to the safe operation of cities, making accurate and efficient flood modeling a critical support for disaster prevention and control. Based on literature data from the Web of Science Core Collection from 2000 to 2026, this study conduct a knowledge graph analysis, systematically examining the evolution of research hotspots and development trends in urban flood modeling. The results show that the field has shifted from the dominance of hydrological and hydrodynamic models to the rapid incorporation of data-driven methods such as machine learning and deep learning. However, the methods related to large AI models have not yet formed independent clustering, and their research in the field of urban flooding is still in the exploratory stage. This study prospectively discusses the application prospects of large AI models and points out the need for further verification in specific scenarios. The study aims to provide a reference for interdisciplinary research integrating urban flood models and large AI models.