Construction and LLM-Based Automatic Extraction of Prevention and Control Measures for Disasters and Accidents in Multi-Hazard Scenarios

Abstract

The increasing complexity of multi-hazard disasters poses significant challenges to sustainable disaster risk governance. However, prevention and control measures are often scattered across heterogeneous and unstructured sources, limiting their systematic reuse and application. To address this issue, this study proposes a structured framework and data-driven analysis approach for organizing prevention and control measures in multi-hazard scenarios. By integrating multi-source information, a four-dimensional framework consisting of human, technical, engineering, and managerial measures was developed, together with a two-dimensional representation model incorporating disaster scenarios. Large language models (LLMs) were employed to automatically extract prevention and control measures from disaster-related documents and construct a multi-hazard prevention dataset. A case study of typhoon–hazardous chemical leakage scenarios yielded 1089 measurement records. Results show that managerial measures had the highest coverage (87.1%), while technical measures mainly focused on critical risk nodes such as leakage monitoring and automatic interlock control. Prevention and preparedness measures accounted for 67.4% of all records, reflecting a proactive risk-governance orientation. Strong associations were observed among the four categories of measures (Jaccard coefficient: 0.624–0.879). The proposed framework supports the structured representation, knowledge organization, and data-driven analysis of prevention and control measures, providing a foundation for sustainable disaster risk governance and resilient emergency management.