Fine-grained flood disaster information extraction incorporating multiple semantic features

Abstract

Flood disasters rank as the most prevalent natural calamities of the twenty-first century, incurring extensive human and economic losses globally. As a crucial source for disaster monitoring, social media data exhibits high variability and ambiguity, with current research lacking targeted multidimensional semantic analysis, resulting in coarse granularity and limited accuracy. To address this problem, this study proposes a framework and method synthesizing multiple semantic features to extract fine-grained disaster information. Static embeddings representing stable semantics and dynamic embeddings representing changing semantics are fused to extract toponyms, with the depth-first search used to generate addresses through the toponym tree. Guiding prompts incorporating domain-specific knowledge of disasters are designed for the large language model, with an iterative feedback process refining location-based disaster information. Finally, the reliability of social media-sourced information is assessed by comparing extracted flooded locations with actual monitoring data. The case study on the Zhengzhou ‘7·20’ flood event demonstrates the effectiveness of our semantic fusion approach, achieving an F1 score of 0.9384 for address extraction, with accuracies of 0.8485 and 0.8788 for waterlogging depth and trapped individuals, respectively. This research offers a practical framework for nuanced perception and timely rescue operations in urban disaster management.