Surrogate modeling for peak or time-dependent storm surge prediction over an extended coastal region using an existing database of synthetic storms

Jia, Gaofeng; Taflanidis, Alexandros A.; Nadal-Caraballo, Norberto C.; Melby, Jeffrey A.; Kennedy, Andrew; Smith, Jane McKee

doi:10.1007/s11069-015-2111-1

Cited by 85 publications

(51 citation statements)

References 35 publications

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“…The storm surge model underpinning the SHP tool builds on the surrogate modeling approach used in the aforementioned CyberEye platform (Jia and Taflanidis, 2013;Jia et al, 2015). The foundation of the implementation is a simplified description of each hurricane scenario by a small number of model parameters corresponding to the characteristics at landfall: (i) landfall location; (ii) track heading; (iii) central pressure; (iv) forward speed; and (v) radius of maximum winds.…”

Section: Coastal Storm Surgementioning

confidence: 99%

Geospatial Environments for Hurricane Risk Assessment: Applications to Situational Awareness and Resilience Planning in New Jersey

Kijewski-Correa

Taflanidis

Vardeman

et al. 2020

Front. Built Environ.

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Kijewski-Correa et al. Geospatial Environments for Hurricane Risk this platform in the State of New Jersey, detailing the cyberinfrastructure design and underlying computational models, as well as the user stories that inspired the platform's functionalities and interfaces. The study concludes with reflections from the process of piloting this project with stakeholders at the state and municipal level to support more risk-responsive and data-informed decision making.

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Section: Coastal Storm Surgementioning

confidence: 99%

Geospatial Environments for Hurricane Risk Assessment: Applications to Situational Awareness and Resilience Planning in New Jersey

Kijewski-Correa

Taflanidis

Vardeman

et al. 2020

Front. Built Environ.

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“…The flowchart for the overall method is illustrated in Figure 2, and the following sections provide brief discussions for the steps. putational efficiency [16,7,8]. First, we establish a database with n total runs of the high-fidelity models.…”

Section: High-dimensional Outputsmentioning

confidence: 99%

Investigation of impact of shoreline alteration on coastal hydrodynamics using Dimension REduced Surrogate based Sensitivity Analysis

Jia

Wang

Stacey

2019

Advances in Water Resources

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“…This paper adopts a kriging surrogate model as the interpolation scheme of JPM-OS, while the optimization scheme, which uses the Gaussian quadrature to interpolate the surge response of any storm, could be regarded as a hybrid of JPM-OS-RS (Resio 2007) and JPM-OS-Q (Toro et al 2010a, b). Jia et al (2016) used the kriging surrogate model with principal component analysis (PCA) to produce good (with R 2 close to 95% or better and RMSE less than 10%) peak surge response in coastal regions at 30 locations along Louisiana coast, but did not produce any probabilistic coastal inundation map. Zhang et al (2018) used kriging surrogate model with the database from North Atlantic Comprehensive Coastal Study (NACCS) (Nadal-Caraballo et al 2015a) to discover the annual exceedance rate with and without SLR.…”

Section: Introductionmentioning

confidence: 99%

An objective and efficient method for estimating probabilistic coastal inundation hazards

2019

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The joint probability method (JPM) is the traditional way to determine the base flood elevation due to storm surge, and it usually requires simulation of storm surge response from tens of thousands of synthetic storms. The simulated storm surge is combined with probabilistic storm rates to create flood maps with various return periods. However, the map production requires enormous computational cost if state-of-the-art hydrodynamic models with high-resolution numerical grids are used; hence, optimal sampling (JPM-OS) with a small number of (~ 100-200) optimal (representative) storms is preferred. This paper presents a significantly improved JPM-OS, where a small number of optimal storms are objectively selected, and simulated storm surge responses of tens of thousands of storms are accurately interpolated from those for the optimal storms using a highly efficient kriging surrogate model. This study focuses on Southwest Florida and considers ~ 150 optimal storms that are selected based on simulations using either the low fidelity (with low resolution and simple physics) SLOSH model or the high fidelity (with high resolution and comprehensive physics) CH3D model. Surge responses to the optimal storms are simulated using both SLOSH and CH3D, and the flood elevations are calculated using JPM-OS with highly efficient kriging interpolations. For verification, the probabilistic inundation maps are compared to those obtained by the traditional JPM and variations of JPM-OS that employ different interpolation schemes, and computed probabilistic water levels are compared to those calculated by historical storm methods. The inundation maps obtained with the JPM-OS differ less than 10% from those obtained with JPM for 20,625 storms, with only 4% of the computational time.

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Surrogate modeling for peak or time-dependent storm surge prediction over an extended coastal region using an existing database of synthetic storms

Cited by 85 publications

References 35 publications

Geospatial Environments for Hurricane Risk Assessment: Applications to Situational Awareness and Resilience Planning in New Jersey

Geospatial Environments for Hurricane Risk Assessment: Applications to Situational Awareness and Resilience Planning in New Jersey

Investigation of impact of shoreline alteration on coastal hydrodynamics using Dimension REduced Surrogate based Sensitivity Analysis

An objective and efficient method for estimating probabilistic coastal inundation hazards

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