Improvements in storm surge surrogate modeling for synthetic storm parameterization, node condition classification and implementation to small size databases

Aikaterini P. Kyprioti, Alexandros A. Taflanidis (), Matthew Plumlee, Taylor G. Asher, Elaine Spiller, Richard A. Luettich, Brian Blanton, Tracy L. Kijewski-Correa, Andrew Kennedy and Lauren Schmied
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Aikaterini P. Kyprioti: University of Notre Dame
Alexandros A. Taflanidis: University of Notre Dame
Matthew Plumlee: Northwestern University
Taylor G. Asher: University of North Carolina
Elaine Spiller: Marquette University
Richard A. Luettich: University of North Carolina
Brian Blanton: University of North Carolina
Tracy L. Kijewski-Correa: University of Notre Dame
Andrew Kennedy: University of Notre Dame
Lauren Schmied: FEMA

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 109, issue 2, No 2, 1349-1386

Abstract: Abstract Surrogate models are becoming increasingly popular for storm surge predictions. Using existing databases of storm simulations, developed typically during regional flood studies, these models provide fast-to-compute, data-driven approximations quantifying the expected storm surge for any new storm (not included in the training database). This paper considers the development of such a surrogate model for Delaware Bay, using a database of 156 simulations driven by synthetic tropical cyclones and offering predictions for a grid that includes close to 300,000 computational nodes within the geographical domain of interest. Kriging (Gaussian Process regression) is adopted as the surrogate modeling technique, and various relevant advancements are established. The appropriate parameterization of the synthetic storm database is examined. For this, instead of the storm features at landfall, the features when the storm is at closest distance to some representative point of the domain of interest are investigated as an alternative parametrization, and are found to produce a better surrogate. For nodes that remained dry for some of the database storms, imputation of the surge using a weighted k nearest neighbor (kNN) interpolation is considered to fill in the missing data. The use of a secondary, classification surrogate model, combining logistic principal component analysis and Kriging, is examined to address instances for which the imputed surge leads to misclassification of the node condition. Finally, concerns related to overfitting for the surrogate model are discussed, stemming from the small size of the available database. These concerns extend to both the calibration of the surrogate model hyper-parameters, as well as to the validation approaches adopted. During this process, the benefits from the use of principal component analysis as a dimensionality reduction technique, and the appropriate transformation and scaling of the surge output are examined in detail.

Keywords: Storm surge surrogate model; Kriging; Gaussian process; Storm parameterization; Overfitting; Binary classification; Dry node imputation (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11069-021-04881-9

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