[1]QI Xiaogang,ZHANG Xu,LI Jiahui.Machine learning bridging urban waterlogging and climate risk: a review and outlook[J].CAAI transactions on intelligent systems,2026,21(5):1106-1129.[doi:10.11992/tis.202511003]
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Machine learning bridging urban waterlogging and climate risk: a review and outlook
CAAI transactions on intelligent systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
21
Number of periods:
2026 5
Page number:
1106-1129
Column:
综述
Public date:
2026-09-05
- Title:
- Machine learning bridging urban waterlogging and climate risk: a review and outlook
- Keywords:
- machine learning; urban waterlogging; climate risk; data assimilation; feature transformation; physical mechanism; data-driven; uncertainty quantification; risk transmission
- CLC:
- TP18；P4；TU998.4
- DOI:
- 10.11992/tis.202511003
- Abstract:
- Climate change and urbanization have led to frequent extreme weather events and severe urban waterlogging risks, while conventional methods fail to bridge the gap between climate science and urban waterlogging management. Through systematic review and critical analysis, this paper for the first time summarizes three core “bridging” modes of machine learning connecting the two domains: multi-scale data assimilation and feature transformation bridge, modeling bridge integrating physical mechanisms and data-driven approaches, and assessment bridge for uncertainty quantification and risk transmission. This paper first analyzes the coupling mechanism between climate science and urban waterlogging research and key challenges, then reviews key technologies supporting these bridging modes, and elaborates how each mode addresses core issues through case studies. This summarized framework provides a novel perspective for understanding and mitigating urban waterlogging risks under climate change.
- References:
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