Articles | Volume 26, issue 11
https://doi.org/10.5194/nhess-26-5421-2026
https://doi.org/10.5194/nhess-26-5421-2026
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

Data sets

lianyi732-hue/flood_project: Code and Data, Version 1.0.0 G. Peng et al. https://doi.org/10.5281/zenodo.23681197

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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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