Generalizable Storm Surge Risk Modeling

Storm surges present a severe risk to coastal communities and infrastructure, underscoring the critical importance of accurately estimating extreme events such as the 100-year return surge. These estimates are essential not only for effective hazard assessment but also for informing resilient coasta...

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Main Authors: Mahlon Scott, Hsin-Hsiung Huang
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/13/3/486
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author Mahlon Scott
Hsin-Hsiung Huang
author_facet Mahlon Scott
Hsin-Hsiung Huang
author_sort Mahlon Scott
collection DOAJ
description Storm surges present a severe risk to coastal communities and infrastructure, underscoring the critical importance of accurately estimating extreme events such as the 100-year return surge. These estimates are essential not only for effective hazard assessment but also for informing resilient coastal design. Inspired by principles of robust statistical modeling, this paper introduces a Bayesian hierarchical model integrated with Gaussian processes to account for spatial random effects. This approach enhances the precision of long return period storm surge estimates and enables the seamless generalization of predictions to nearby unmonitored coastal regions, much like the way advanced Bayesian frameworks are applied to high-dimensional neuroimaging or spatiotemporal data, bridging gaps between observations and uncharted territories.
format Article
id doaj-art-1666dfa3c4864249b60b870851ec1365
institution OA Journals
issn 2227-7390
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spelling doaj-art-1666dfa3c4864249b60b870851ec13652025-08-20T02:12:30ZengMDPI AGMathematics2227-73902025-01-0113348610.3390/math13030486Generalizable Storm Surge Risk ModelingMahlon Scott0Hsin-Hsiung Huang1Department of Statistics and Data Science, University of Central Florida, Orlando, FL 32816, USADepartment of Statistics and Data Science, University of Central Florida, Orlando, FL 32816, USAStorm surges present a severe risk to coastal communities and infrastructure, underscoring the critical importance of accurately estimating extreme events such as the 100-year return surge. These estimates are essential not only for effective hazard assessment but also for informing resilient coastal design. Inspired by principles of robust statistical modeling, this paper introduces a Bayesian hierarchical model integrated with Gaussian processes to account for spatial random effects. This approach enhances the precision of long return period storm surge estimates and enables the seamless generalization of predictions to nearby unmonitored coastal regions, much like the way advanced Bayesian frameworks are applied to high-dimensional neuroimaging or spatiotemporal data, bridging gaps between observations and uncharted territories.https://www.mdpi.com/2227-7390/13/3/486coastal hazard assessmentBayesian hierarchical modelextreme eventsGaussian processlong return period predictionstorm surge estimation
spellingShingle Mahlon Scott
Hsin-Hsiung Huang
Generalizable Storm Surge Risk Modeling
Mathematics
coastal hazard assessment
Bayesian hierarchical model
extreme events
Gaussian process
long return period prediction
storm surge estimation
title Generalizable Storm Surge Risk Modeling
title_full Generalizable Storm Surge Risk Modeling
title_fullStr Generalizable Storm Surge Risk Modeling
title_full_unstemmed Generalizable Storm Surge Risk Modeling
title_short Generalizable Storm Surge Risk Modeling
title_sort generalizable storm surge risk modeling
topic coastal hazard assessment
Bayesian hierarchical model
extreme events
Gaussian process
long return period prediction
storm surge estimation
url https://www.mdpi.com/2227-7390/13/3/486
work_keys_str_mv AT mahlonscott generalizablestormsurgeriskmodeling
AT hsinhsiunghuang generalizablestormsurgeriskmodeling