A spatiotemporal framework for identifying urban flood priority areas in megacities using social media data

Urban flood (UF) poses major challenges to sustainable development and public safety. Geographic information-based studies commonly apply machine learning (ML) with UF samples and multi-domain factors to assess urban flood susceptibility (UFS) and identify priority regions for UF management. However, most existing approaches rely solely on the latest UFS map, overlooking the dynamic nature of UF and thereby reducing the reliability of the identified priority areas. To address this limitation, we propose an ML-based framework for spatiotemporal UFS estimation to delineate more accurate and practical UF priority regions. The framework integrates: (1) Natural Language Processing to extract flood locations from social media ( Weibo) , and (2) a Spatiotemporal Susceptibility Extraction (SSE) method that combines the spatial and temporal characteristics of UFS. Guangzhou, a megacity in China with frequent UF events and extensive Weibo usage, is employed as the case study. We found: (1) incorporating UF locations extracted from social media effectively complements official records, as evidenced by both the increased information content of the positive class across multiple flood-conditioning factors and the improved classifier performance and robustness; and (2) compared with priority areas directly delineated from the latest-year UFS map, the SSE-derived UF priority areas show higher accuracy when evaluated against ground-truth UF data, further supported by high-resolution remote sensing imagery, while also exhibiting more spatially concentrated and continuous patterns. The proposed framework enables rapid and reliable identification of UF priority areas and provides practical decision support for UF prevention and management in rapidly urbanizing megacities under resource constraints.