Rapid construction and analysis of cascading events for disaster investigation and assessment

Between the occurrence of a disaster and the initiation of formal investigation and assessment, the overall evolution of the disaster often remain unclear, which hinders the timely progress of investigative work. To address this challenge, this study proposes a framework for constructing disaster event chains from limited post-disaster information. The framework first defines event patterns based on disaster system theory, then employs the unified information extraction model to automatically extract event chain nodes and their relationships from news texts. DBSCAN clustering is further applied to achieve semantic normalization and entity standardization, enabling the reconstruction of the disaster event chain. Finally, a fuzzy comprehensive evaluation is conducted to assess the effectiveness of the proposed framework. The results demonstrate that the framework can provide valuable initial analytical insights and auxiliary support for disaster investigation efforts.