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Generalizable Storm Surge Risk Modeling

Author

Listed:
  • Mahlon Scott

    (Department of Statistics and Data Science, University of Central Florida, Orlando, FL 32816, USA
    These authors contributed equally to this work.)

  • Hsin-Hsiung Huang

    (Department of Statistics and Data Science, University of Central Florida, Orlando, FL 32816, USA
    These authors contributed equally to this work.)

Abstract

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.

Suggested Citation

  • Mahlon Scott & Hsin-Hsiung Huang, 2025. "Generalizable Storm Surge Risk Modeling," Mathematics, MDPI, vol. 13(3), pages 1-10, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:3:p:486-:d:1581342
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