Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 26, issue 11
Articles | Volume 26, issue 11
https://doi.org/10.5194/nhess-26-5421-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-5421-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 11
Research article
 | 
30 Oct 2026
Research article |  | 30 Oct 2026

Multi-data source based quantifying urban flood severity in major Chinese cities (2000–2024) using a hybrid machine-learning weighting framework

Guoqiang Peng, Yujing Sun, Mao Wang, Min Chen, and Fengyuan Zhang

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Short summary
This study combines official records, news reports, social media posts, and search data to track urban flood events in twenty major Chinese cities from 2000 to 2024. It builds a comparable database and uses an interpretable machine learning framework to measure flood severity across cities and years. Results show a clear north south contrast: southern river and coastal cities face frequent severe floods, while northern cities usually face lower risk but suffer sudden extremes during rare storms.
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