An automated end-to-end pipeline for identifying fine-grained waterlogging locations from Chinese social media

Social media-based waterlogging locations identification provides a timely, cost-effective solution for urban flood emergency management. However, Chinese toponymic complexities challenge waterlogging location extraction from social media. This study proposes a pipeline integrating reverse filtering, text classification, sentence segmentation, Named Entity Recognition (NER), and Waterlogging Location Refinement (WLR) to identify Fine-grained waterlogging locations (Fg_wls). The WLR algorithm innovatively combines language rules with a Chinese NER model, enhancing completeness, accuracy, and granularity while avoiding time-consuming dataset annotation. Using Shenzhen as a case study, 622 Fg_wls were extracted from 7,243 Weibo posts between 2018 and 2022, including 395 point-level, 146 line-level, and 81 polygon-level locations. The WLR algorithm helps improve the accuracy of fine-grained location identification from 59.4% when using only the PFR-NER model to 92.2% when adding WLR. The proposed pipeline delivers urban-level flood risk information to emergency responders, enabling precise disaster mitigation.