FloodWatch: Devising an Autonomous Cyber Physical System for Real-Time Flood in an Operational Framework

Karande, Abhir, School of Engineering and Applied Science, University of Virginia
Neeley, Kathryn, University of Virginia

Flooding has devastating impacts in
Southeast Asia, in part due to the lack of
appropriate infrastructure. As a part of my
involvement in the FloodWatch research
group, I am devising an autonomous cyber
physical system for real-time flood
intelligence that can be leveraged by citizens
in a progressive webapp. FloodWatch is a
smart city service centered around the
development of cyber physical system
infrastructure for disaster intelligence and
forecasting. The service provides a frontend
progressive web application (PWA), live
sensor readings through LoRAWAN router
technology, and predictive insights on flood
susceptibility. To produce insights on
susceptibility, I am working on several
machine learning pipelines to ensure
accuracy, explainability and corroboration.
The current approach combines an existing
hydrological model called HEC-RAS with a
customized Artificial Neural Network
(ANN) to produce geographical flood
inundation that is then interpreted and
overlaid on the map of Vietnam using a
region tileset automation script. Additionally,
I am devising a corroboration mechanism for
crowdsourced flood reports by using LiDAR
water level interpretation, computer vision
for flood label detection of live camera feeds.

BS (Bachelor of Science)
All rights reserved (no additional license for public reuse)
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