Integrating AI and ML for Enhanced Multi-Hazard Risk Management

The growing interconnections between natural hazards, socio-economic systems, and vulnerabilities are increasing the frequency and impact of multi-hazard and compound risk events. Addressing this complexity requires innovative data-driven approaches that can integrate heterogeneous information and support both risk mitigation and preparedness across scales.

This talk showcases how Machine Learning (ML) and Artificial Intelligence, including Large Language Models (LLMs), can be effectively implemented in disaster risk management (DRM), with a focus on applications supporting the EU preparedness agenda at both European and global levels. First, I will present ML-based approaches for impact-oriented multi-hazard risk assessment, highlighting ensemble models developed to quantify compound hazard effects on flood losses at the subnational scale across Europe. I will then discuss ML applications for crisis anticipation, including forecasting food insecurity and conflict-induced human displacement, demonstrating how predictive models can support early warning and preparedness planning.

In a second part, the talk will illustrate how LLM-based methods can enhance data availability and knowledge integration for multi-hazard risk analysis. This includes automated geocoding of disaster locations from unstructured text to enable accurate subnational risk modelling, as well as the use of LLMs with Retrieval-Augmented Generation to extract factual crisis storylines and construct knowledge graphs from news and reports, supporting the analysis of cascading impacts and risk drivers.

Together, these examples demonstrate how AI-driven technologies can move beyond methodological innovation to deliver operational tools and evidence that directly support disaster risk reduction, preparedness, and decision-making, contributing to more resilient societies and informed policy-making that can adapt to evolving risk landscapes.