SaferPlaces Agentic AI: Democratising Global Flood Risk Intelligence for Disaster Risk Reduction and Management

Urban areas are increasingly exposed to flood risk due to climate change, land take, and ageing drainage infrastructure. Although large volumes of geospatial data, numerical models, and meteorological information are available, their operational use in disaster risk reduction (DRR) and disaster risk management (DRM) remains limited. Civil protection officers and first responders often rely on static maps or complex GIS workflows that are poorly suited for rapid, scenario-based decision-making during emergencies. A key challenge is the lack of accessible and intuitive tools capable of translating advanced flood modelling into actionable intelligence in real time.

This contribution presents SaferPlaces Agentic AI, an agentic Large Language Model (LLM)-based digital twin framework designed to democratise access to flood risk intelligence and make professional-grade flood simulations usable by non-technical stakeholders. The system is implemented within the SaferPlaces platform and operates at global scale, allowing flood risk analyses to be activated on demand for any Area of Interest (AOI) worldwide through natural language interaction.

The framework is centred on an autonomous LLM agent that interprets user intents and orchestrates heterogeneous geospatial data sources, meteorological observations and forecasts, and hydrological–hydrodynamic modelling services. Users can trigger complex workflows conversationally—such as simulating forecast-driven pluvial flood scenarios, identifying exposed critical infrastructure, or testing mitigation measures—without requiring GIS or modelling expertise. Outputs include flood extent, water depth, flow velocity, and receptor-level impact metrics, fully interoperable with standard GIS environments and enhanced through immersive 3D and virtual reality visualisation. The modular, tool-based design of the agent enables the integration of additional analytical capabilities, external services, and hazard-specific models over time, supporting future multi-hazard applications such as wildfires, heatwaves, droughts, and compound risk scenarios.

Persistent project-level memory enables iterative scenario exploration and rapid adaptation of analyses during evolving emergency situations. To ensure reliability, transparency, and trust in operational contexts, the system adopts a configurable human-in-the-loop approach, allowing users to validate assumptions and control the level of automation.

Through urban flood digital twin applications, early-warning support, and mitigation scenario testing, SaferPlaces Agentic AI demonstrates how agentic systems can bridge the gap between complex geoscientific modelling and real-world emergency decision-making. The approach supports more inclusive, scalable, and effective flood DRR and DRM, contributing to improved preparedness and resilience in a changing climate.