Using Multimodal Data and AI to Dynamically Map Flood Risk
Keywords:Flooding, Rainfall Radar Data, Social Media, Transformer, Sentiment Analysis, Autoencoders
AbstractClassical 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.
How to Cite
Bryan-Smith, L. (2022). Using Multimodal Data and AI to Dynamically Map Flood Risk. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12874-12875. https://doi.org/10.1609/aaai.v36i11.21574
The Twenty - Seventh AAAI / SIGAI Doctoral Consortium