TY - JOUR AU - Bryan-Smith, Lydia PY - 2022/06/28 Y2 - 2024/03/28 TI - Using Multimodal Data and AI to Dynamically Map Flood Risk JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 36 IS - 11 SE - The Twenty - Seventh AAAI / SIGAI Doctoral Consortium DO - 10.1609/aaai.v36i11.21574 UR - https://ojs.aaai.org/index.php/AAAI/article/view/21574 SP - 12874-12875 AB - 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. ER -