Project description
Predicting the weather using artificial intelligence
The EU-funded ISOBAR project aims to exploit Artificial Intelligence and develop five main AI components to support the Demand Capacity Balancing (DCB) supply chain in non-nominal and critical situations: enhancing and anticipating the prediction of convection, better characterising capacity and demand, identifying weather-related imbalances between capacity and demand and selecting capacity-demand mitigation measures at local and network levels. The project will be built on four main pillars. First, it will reinforce collaborative Air Traffic Flow and Capacity Management (ATFM) processes at pre-tactical and tactical levels into the local and network roles integrating dynamic weather cells. Second, it will characterise the demand and capacity imbalances. Third, it will set up a user-driven mitigation plan. Ultimately, the project will develop an operational and technical roadmap for the integration of ancillary services into SWIM.
Objective
ISOBAR aims at the provision of a service- and AI-based Network Operations Plan, by integrating enhanced convective weather forecasts for predicting imbalances between capacity and demand and exploiting AI to select mitigation measures at local and network level in a collaborative ATFCM operations paradigm. To achieve this vision, four objectives are set:
a) Reinforce collaborative ATFCM processes at pre-tactical and tactical levels into the LTM (local) and Network Management (network) roles integrating dynamic weather cells.
b) Characterisation of demand and capacity imbalances at pre-tactical level [-1D, -30min] depending on the input of probabilistic weather cells by using applied AI methods and ATM and weather data integration.
c) User-driven mitigation plan considering AUs priorities (and fluctuations in demand based on weather forecasts) and predicted effectiveness of ATFCM regulations, considering flow constraints and network effects.
d) Develop an operational and technical roadmap for the integration of ancillary services (providing AI-based hotspot detection and adaptative mitigation measures) into the NM platform, by defining interfaces, functional and performance requirements.
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Funding Scheme
RIA - Research and Innovation actionCoordinator
28022 Madrid
Spain