An AI sub-group leveraging AI to build more resilient and effective early warning systems. The project uses advanced AI to identify and quantify populations lacking connectivity, enhancing targeted early warning capabilities and intervention strategies.
Activity Type Research/Reports/AssessmentsPolicy/Regulatory GuidanceMedEWSa is an Innovation action (IA) project led by the Justus Liebig University Giessen (JLU) and a consortium of 30 partners. Building on existing tools, MedEWSa will develop a fully integrated impact-based multi-hazard early warning system (EWS). It will achieve advances beyond the state-of-the-art in the areas of AI-based decision support solutions for improved impact prediction, methods for impact prediction and early warnings, modelling and prediction of multiple hazard dynamics and their interdependencies. MedEWSa will explore hybrid methods combining data-driven approaches and the laws of physics, using tools learned from observations and physics-based model results. Ultimately, it will deploy an analysis to provide integrated, accurate, and rapid early warnings for the pan-European-Mediterranean-African region. MedEWSa will directly contribute to the United Nations’ Sustainable Development Goals and align with the UN’s Early Warnings for All initiative. The project will also develop innovative financial solutions through risk transfer to capital markets. MedEWSa will also set out a roadmap for contributing to different standardisation activities to identify significant opportunities to push contributions into future standards, pre-normative activities, and open collaborative development environments.
Activity Type Research/Reports/AssessmentsPolicy/Regulatory GuidanceInfrastructure/Systems DevelopmentA flagship collaborative platform where thousands of students and professionals design and test AI models for real-world use cases, including communication networks and Geospatial AI. The challenges aim to strengthen technical capacities and contribute to ITU’s standards development.
Activity Type Trainings/workshopsTechnical assistanceResearch/Reports/AssessmentsThis Focus Group laid the groundwork for best practices in the use of AI for natural disaster management, including data collection, modeling, and communication. It concluded its work in 2024.
Activity Type Policy/Regulatory GuidanceResearch/Reports/Assessments