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Flood fOREcasting with Satellite Imagery and Generative High-resolution Techniques

Project description

AI brings real-time flood forecasting to the next level

As one of the most destructive natural disasters, flooding continues to outpace the predictive capabilities of traditional hydraulic simulations. Supported by the Marie Skłodowska-Curie Actions programme, the FORESIGHT project aims to use generative AI to produce high-resolution inundation maps up to five days in advance. It will combine satellite data with AI trained on physics-based models to generate rapid, precise forecasts without the need for time-consuming traditional simulations. This technology is being tested across the Indus, Niger, Rhine, and Severn river basins and can even predict flooding in areas lacking ground sensors. The project’s overall goal is to help authorities make faster, life-saving decisions and mitigate economic damage before inundation begins.

Objective

Floods are among the most devastating natural disasters, causing widespread destruction and economic losses globally. Despite advancements in flood early warning systems, they remain limited in their ability to mitigate such losses, primarily due to a lack of accurate real-time flood inundation maps (FIMs) forecasting and reliable uncertainty quantification. The overall goal of the FORESIGHT project is to develop a framework to address this critical gap by leveraging cutting-edge Generative Artificial Intelligence (GenAI) techniques to forecast real-time FIMs with high resolution (30 meter/6hr) up to 5 days ahead. The proposed approach offers multiple advantages: it enables fast generation of FIMs by employing GenAI and omitting the time-consuming physical hydraulic model component of traditional approaches. It ensures accuracy by modifying dynamic river networks and improving precipitation forecast uncertainties. Physical consistency is maintained by training GenAI with data output from physical models. Crucially, it provides reliable uncertainty quantification for the generated forecasts. This innovative approach will be applied across four diverse large river basins - the Rhine, Indus, Niger, and Severn - to ensure broad applicability. Additionally, I will test the approach in ungauged basins to assess its potential applicability in data-scarce regions. By utilizing only satellite earth observation data and open-source global coverage data, the project aims to pave the way for the development of global operational flood forecasting systems.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

THE UNIVERSITY OF READING
Net EU contribution
€ 276 187,92
Address
WHITEKNIGHTS CAMPUS WHITEKNIGHTS HOUSE
RG6 6AH Reading
United Kingdom
Region
South East (England) Berkshire, Buckinghamshire and Oxfordshire Berkshire
Activity type
Higher or Secondary Education Establishments
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Total cost
No data

Partners (2)