Using Multimodal Data and AI to Dynamically Map Flood Risk
[AAAI-22] Doctoral Consortium
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 3Red 6
Poster Session 1Red 6