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Demo Data from EDBT 2026 / reviewers in the wild / expert

Lydia Bryan-Smith

dblp:324/1794 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2022
0000-0003-1953-403XORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Environmental and earth informatics · 50% Smart cities and intelligent transportation · 50%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Smart cities and intelligent transportation › disaster management
disaster response		 0.612022
Using Multimodal Data and AI to Dynamically Map Flood Risk · AAAI 2022

Methods — techniques the papers use, named apart from their topics

multimodal data fusion · 0.6AI-based prediction · 0.6
YearPublicationVenuePosition
2022 Using Multimodal Data and AI to Dynamically Map Flood Risk
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.
Lydia Bryan-Smith
 AAAI1