Turning Global News into Disaster Insights: Large Language Models and Knowledge Graphs for Multi-Hazard Analysis

We explore the use of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to extract, structure, and analyze disaster information from multilingual news sources. Using over 3,000 events from the Emergency Events Database (EM-DAT, 2014–2024), we process Europe Media Monitor (EMM) news to generate structured disaster storylines and knowledge graphs that capture complex interactions among hazards, impacts, and responses—details often missing from traditional datasets. RAG enables the construction of coherent narratives detailing hazard characteristics, affected regions, fatalities, and economic losses, complementing conventional approaches such as remote sensing with richer contextual information. These structured outputs support retrospective analysis, multi-hazard risk assessment, and decision-making for disaster management. In line with the FAIR (Findable, Accessible, Interoperable and Reusable) principles, all workflows are openly accessible via an interactive exploration dashboard, and the data generated are made available through the Joint Research Data Catalogue. This study illustrates how LLMs and NLP can transform unstructured reporting into organized, reusable formats, enhancing situational awareness, early warning, and operational planning. It highlights both the opportunities and methodological considerations—including automation, reproducibility, and integration with existing hazard monitoring systems—demonstrating the potential of text-as-data approaches for advancing natural hazard research in geosciences