Bridging Risk Knowledge and Operational Outcomes through Retrieval-Augmented Generation and Knowledge Graphs for Early Warning Systems

In emergency management, the gap between scientific risk knowledge and operational decision-making remains a persistent challenge for early warning systems. While vast amounts of data—ranging from risk assessments to historical event records—are available, they are often underutilized due to the complexity and fragmentation of information sources. This research proposes an innovative approach to bridge this gap by integrating Retrieval-Augmented Generation (RAG) with domain-specific knowledge graphs to enhance situational awareness and decision support in emergency operations centers.

The proposed solution focuses on developing a graph-based RAG pipeline that interacts with an external repository of risk data on Italy, specifically tailored for emergency response personnel, including civil protection agencies and the Italian Red Cross. The repository incorporates emergency plans, historical events, risk assessments, civil protection guidelines and legislation, and real-time updates from external sources such as news and media. By structuring the data through a knowledge graph aligned with established risk frameworks (e.g., RISK INFORM), the system enables precise, explainable, and contextual information retrieval.

Key features of the tool include an explainability module for transparency, a PDF parser for document integration, and a web interface that allows users to interact with the system through natural language queries. For example, an analyst responding to severe floods in Northern Italy could query the system for demographic data, flood risk hotspots, and critical infrastructure at risk, receiving actionable insights grounded in both historical and live data.

The project demonstrates how AI-driven approaches, when combined with structured domain knowledge, can make early warning systems more effective by improving accessibility, scalability, and interoperability across sectors. The use of knowledge graphs ensures data explainability and traceability, addressing key challenges in emergency management, such as trust in AI outputs and timely decision-making. The platform, currently under development, aims to serve as a proof-of-concept for future applications in multi-hazard early warning systems.

This research contributes to the evolving field of AI-enhanced early warning systems, offering a novel, trans-disciplinary methodology that combines data science, emergency management, and humanitarian operations to improve anticipatory action and disaster preparedness.