On 28–29 September 2026, the AI-WARN consortium met in Dubrovnik to further develop the monitoring and data integration framework for the project’s case studies in Dubrovnik and Brussels. The meeting focused on how different monitoring technologies and data sources will be combined to support the project’s AI-based early warning system.
AI-WARN consortium meets in Dubrovnik to shape the monitoring and data integration framework
On 28–29 September 2026, the AI-WARN consortium met in Dubrovnik to further develop the monitoring and data integration framework for the project's two case studies: Dubrovnik and Brussels.
A key focus was defining how different monitoring technologies and data sources will work together within the system. The consortium discussed sensor types, monitoring locations, spatial and temporal coverage, data resolution and acquisition frequency. Partners also discussed adaptive monitoring strategies that increase data collection when environmental conditions indicate elevated risk.
Dubrovnik: The discussion focused on two hazards, unstable retaining walls in the urban area and rockfall-prone slopes. The planned monitoring combines IoT-enabled sensors measuring rainfall, wall tilting or slope displacement, and vibration. These are complemented by laser scanning, UAV-based monitoring and potentially satellite observations, with detailed UAV surveys triggered by events such as intense rainfall.
Brussels: The focus is urban flood monitoring in the Laeken area. The approach combines rainfall observations, sewer water levels and flow, surface water information, meteorological forecasts and hydraulic modelling. It also draws on a broad range of spatial and historical data on infrastructure, population and past flood events.
Data integration: The Consortium also addressed how these heterogeneous data sources will be integrated, standardised and synchronised. The aim is a common, interoperable data environment for the subsequent AI-based analysis, interpretation and decision-making.
The meeting marked another important step towards translating the AI-WARN concept into concrete monitoring and data integration solutions for real-world climate-induced hazards. Next up: sensor installation at the pilot sites and the definition of thresholds for early warnings.
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