Advanced Local Digital Twins using AI for Early Warning and Preparedness (IA)

Expected Outcome:Open AI models that can help predict, respond to, and mitigate impacts before a disaster occurs, enabling proactive decision-making and effective disaster management effectively. Protection of citizens from natural hazards through proactive measures, preparedness strategies, and urban resilience, planningProject results are expected to contribute to one of the following outcomes:Enhanced protection of citizens from the natural hazard of flooding by facilitating proactive decision-making and effective disaster management through open AI-driven models for urban resilience strategies and planning that can help predict, respond to, and mitigate impacts before a disaster occurs.Improved modelling and prediction of urban and riverine flooding by expanding the capabilities of Local Digital Twins with sophisticated AI algorithms and relevant data detailing hydrological and hydraulic processes.Strengthened integration of diverse and essential datasets including detailed terrain, land cover, urban features, soil data, and real-time meteorological information (rainfall and temperature, river geometry, and flow) sourced from national hydrometric networks, urban drainage infrastructure, and flood protection assets. This integration aims to enhance flood analysis, simulation, and preparedness particularly in response to climate change and flood scenarios like heavy rainfall impacting nearby river basins. The projects will leverage high-resolution climatic and meteorological models to assess extreme weather, while also drawing on relevant initiatives such as the Global Flood Awareness System and Destination Earth.Scope:In line with the Apply AI Strategy, proposals should develop and implement projects that advance innovative AI algorithms and models from concept to large-scale testing and validation. These solutions will be applied to the creation of Local Digital Twins for flood preparedness, enabling the simulation of flood scenarios, identification of areas at