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Breaking Down the Computational Barriers to Real-Time Urban Flood Forecasting

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Abstract
Flooding impacts are on the rise globally, and concentrated in urban areas. Currently, there are no operational systems to forecast flooding at spatial resolutions that can facilitate emergency preparedness and response actions mitigating flood impacts. We present a framework for real-time flood modeling and uncertainty quantification that combines the physics of fluid motion with advances in probabilistic methods. The framework overcomes the prohibitive computational demands of high-fidelity modeling in real-time by using a probabilistic learning method relying on surrogate models that are trained prior to a flood event. This shifts the overwhelming burden of computation to the trivial problem of data storage, and enables forecasting of both flood hazard and its uncertainty at scales that are vital for time-critical decision-making before and during extreme events. The framework has the potential to improve flood prediction and analysis and can be extended to other hazard assessments requiring intense high-fidelity computations in real-time.
Author(s)
Valeriy Y. IvanovDonghui XuM. Chase DwelleKhachik SargsyanDaniel B. WrightNikolaos Katopodes김종호쩐 옥 빈April WarnockSimone FatichiPaolo BurlandoEnrica CaporaliPedro RestrepoBrett F. SandersMolly M. ChaneyAna M. B. NunesFernando NardiEnrique R. VivoniErkan IstanbulluogluGautam B
Issued Date
2021
Type
Article
Keyword
estimation and forecastingextreme eventsfloodsmegacities and urban environmentuncertainty assessment
DOI
10.1029/2021GL093585
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9201
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_osti_scitechconnect_1826419&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Breaking%20Down%20the%20Computational%20Barriers%20to%20Real-Time%20Urban%20Flood%20Forecasting&offset=0&pcAvailability=true
Publisher
GEOPHYSICAL RESEARCH LETTERS
Location
미국
Language
영어
ISSN
0094-8276
Citation Volume
48
Citation Number
20
Citation Start Page
e2021GL093
Citation End Page
e2021GL093
Appears in Collections:
Engineering > Civil and Environmental Engineering
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