2022
AAAI
AAAI 2022
Using Multimodal Data and AI to Dynamically Map Flood Risk
Abstract
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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Interdisciplinary Bridge
— Artificial Intelligence and Data Science & Analytics and Machine Learning
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Keyword Pioneer
— environmental modeling
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Hot Topic Early Bird
— heterogeneous datum
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio