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

Urban flooding poses a critical challenge to sustainable urban development, yet long-term comparative assessments remain limited by fragmented and heterogeneous disaster records. To address this gap, this study integrates multi-source flood-event data from 20 major Chinese cities during 2000–2024 and develops an impact-based framework for assessing urban flood severity. Official statistics, news reports, and social media records are combined to construct an event-level flood database, which supports the development of a Flood Severity Index (FSI). A hybrid weighting framework that combines conventional weighting schemes with machine-learning-assisted indicator attribution is developed to improve the robustness and interpretability of severity assessment. The results reveal pronounced spatial heterogeneity: southern cities exhibit higher average severity levels and more frequent high-severity events, whereas northern cities generally show lower baseline severity but remain vulnerable to episodic extreme flood impacts. Temporal analysis indicates substantial interannual variability rather than a consistent long-term increasing or decreasing trend. These findings highlight the need for region-specific flood management strategies, including reducing persistent high-severity impacts in southern cities and improving preparedness for abrupt extreme events in northern cities. This study demonstrates the value of integrating fragmented historical records into a comparable framework for urban flood severity assessment.