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    Multi-hazard approach and cumulative / cascading impacts - European Commission

    Funds research to develop AI-driven, integrated models and tools for predicting and managing multiple, cascading hazards to enhance disaster resilience across physical, economic, and social domains.

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    Funder: European Commission

    Due Dates: November 5, 2026 (Full application submission deadline; 17:00 Brussels time)

    Funding Amounts: Total budget €8,000,000; indicative grant size around €4,000,000; typically 2 projects funded.

    Summary: Funds research to develop integrated, AI-powered models and tools for predicting, managing, and enhancing resilience to multiple, cascading hazards across physical, economic, and social domains.

    Key Information: Requires consortium with disaster risk/crisis authorities and local/regional disaster response representatives from at least 3 EU/Associated countries.


    Description

    This European Commission initiative under Horizon Europe supports innovative research aimed at advancing multi-hazard risk assessment and disaster resilience. The focus is on integrating single-hazard models into next-generation predictive systems, capable of analyzing cascading and cumulative effects across meteorological, geophysical, and technological hazards. Projects are expected to develop and validate integrated forecasting models, leveraging real-time data, AI-driven analytics, and remote sensing, to improve hazard prediction, system interoperability, and data exchange.

    The grant encourages a holistic, cross-cutting approach to disaster risk management, considering climate change, environmental degradation, and social vulnerabilities. Outcomes should include comprehensive risk and resilience metrics to support decision-making, as well as advanced tools for assessing combined impacts on critical infrastructure and communities. Collaboration among stakeholders (scientific, policy, emergency, and infrastructure sectors) is essential, and projects should plan for the uptake of results and ensure that data produced is FAIR (Findable, Accessible, Interoperable, and Re-usable).


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