Using Multimodal Data and AI to Dynamically Map Flood Risk

Lydia Bryan-Smith

[AAAI-22] Doctoral Consortium

Keywords
Poster Session 3 @ Red 6, Poster Session 1 @ Red 6, Poster Session 3, Poster Session 1

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Abstract: Classical measurements and modelling that underpin present flood warning and alert systems are based on fixed and spatially restricted static sensor networks. Computationally expensive physics-based simulations are often used that can't react in real-time to changes in environmental conditions. We want to explore contemporary artificial intelligence (AI) for predicting flood risk in real time by using a diverse range of data sources. By combining heterogeneous data sources, we aim to nowcast rapidly changing flood conditions and gain a grater understanding of urgent humanitarian needs.

Sessions where this paper appears

  • Poster Session 3

    Red 6

  • Poster Session 1

    Red 6