ML Anthology

Using Multimodal Data and AI to Dynamically mAP Flood Risk

Lydia Bryan-Smith
AAAI 2022 pp. 12874-12875
doi:10.1609/AAAI.V36I11.21574 /aaai/2022/bryansmith2022aaai-using/

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.

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Cite

Text

Bryan-Smith. "Using Multimodal Data and AI to Dynamically mAP Flood Risk." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21574

Markdown

[Bryan-Smith. "Using Multimodal Data and AI to Dynamically mAP Flood Risk." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/bryansmith2022aaai-using/) doi:10.1609/AAAI.V36I11.21574

BibTeX

@inproceedings{bryansmith2022aaai-using,
  title     = {{Using Multimodal Data and AI to Dynamically mAP Flood Risk}},
  author    = {Bryan-Smith, Lydia},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {12874-12875},
  doi       = {10.1609/AAAI.V36I11.21574},
  url       = {https://mlanthology.org/aaai/2022/bryansmith2022aaai-using/}
}