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Advances in surrogate modeling for storm surge prediction: storm selection and addressing characteristics related to climate change

Author

Listed:
  • Jize Zhang

    (University of Notre Dame)

  • Alexandros A. Taflanidis

    (University of Notre Dame)

  • Norberto C. Nadal-Caraballo

    (United States Army Corps of Engineers)

  • Jeffrey A. Melby

    (Noble Consultants-G.E.C., Inc.)

  • Fatimata Diop

    (United States Army Corps of Engineers)

Abstract

This paper establishes various advancements for the application of surrogate modeling techniques for storm surge prediction utilizing an existing database of high-fidelity, synthetic storms (tropical cyclones). Kriging, also known as Gaussian process regression, is specifically chosen as the surrogate model in this study. Emphasis is first placed on the storm selection for developing the database of synthetic storms. An adaptive, sequential selection is examined here that iteratively identifies the storm (or multiple storms) that is expected to provide the greatest enhancement of the prediction accuracy when that storm is added into the already available database. Appropriate error statistics are discussed for assessing convergence of this iterative selection, and its performance is compared to the joint probability method with optimal sampling, utilizing the required number of synthetic storms to achieve the same level of accuracy as comparison metric. The impact on risk estimation is also examined. The discussion then moves to adjustments of the surrogate modeling framework to support two implementation issues that might become more relevant due to climate change considerations: future storm intensification and sea level rise (SLR). For storm intensification, the use of the surrogate model for prediction extrapolation is examined. Tuning of the surrogate model characteristics using cross-validation techniques and modification of the tuning to prioritize storms with specific characteristics are proposed, whereas an augmentation of the database with new/additional storms is also considered. With respect to SLR, the recently developed database for the US Army Corps of Engineers’ North Atlantic Comprehensive Coastal Study is exploited to demonstrate how surrogate modeling can support predictions that include SLR considerations.

Suggested Citation

  • Jize Zhang & Alexandros A. Taflanidis & Norberto C. Nadal-Caraballo & Jeffrey A. Melby & Fatimata Diop, 2018. "Advances in surrogate modeling for storm surge prediction: storm selection and addressing characteristics related to climate change," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(3), pages 1225-1253, December.
  • Handle: RePEc:spr:nathaz:v:94:y:2018:i:3:d:10.1007_s11069-018-3470-1
    DOI: 10.1007/s11069-018-3470-1
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    References listed on IDEAS

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    Cited by:

    1. Aikaterini P. Kyprioti & Alexandros A. Taflanidis & Matthew Plumlee & Taylor G. Asher & Elaine Spiller & Richard A. Luettich & Brian Blanton & Tracy L. Kijewski-Correa & Andrew Kennedy & Lauren Schmie, 2021. "Improvements in storm surge surrogate modeling for synthetic storm parameterization, node condition classification and implementation to small size databases," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(2), pages 1349-1386, November.
    2. Kun Yang & Vladimir Paramygin & Y. Peter Sheng, 2019. "An objective and efficient method for estimating probabilistic coastal inundation hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(2), pages 1105-1130, November.
    3. Aikaterini P. Kyprioti & Alexandros A. Taflanidis & Norberto C. Nadal-Caraballo & Madison O. Campbell, 2021. "Incorporation of sea level rise in storm surge surrogate modeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 531-563, January.
    4. Jung, WoongHee & Taflanidis, Alexandros A. & Kyprioti, Aikaterini P. & Zhang, Jize, 2024. "Adaptive multi-fidelity Monte Carlo for real-time probabilistic storm surge predictions," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    5. S. Lecacheux & J. Rohmer & F. Paris & R. Pedreros & H. Quetelard & F. Bonnardot, 2021. "Toward the probabilistic forecasting of cyclone-induced marine flooding by overtopping at Reunion Island aided by a time-varying random-forest classification approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 227-251, January.

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