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OS52B-0512: Physics-informed Machine Learning for Estimation of Spatially-distributed Sea Level Rise Rates and their Associated High Tide Flooding
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  • Sadaf Mahmoudi,
  • David F Muñoz,
  • Hamed Moftakhari,
  • Hamid Moradkhani
Sadaf Mahmoudi
Center for Complex Hydrosystems Research, Department of Civil, Construction and Environmental Engineering, University of Alabama

Corresponding Author:[email protected]

Author Profile
David F Muñoz
Center for Complex Hydrosystems Research, Department of Civil, Construction and Environmental Engineering, University of Alabama
Hamed Moftakhari
Center for Complex Hydrosystems Research, Department of Civil, Construction and Environmental Engineering, University of Alabama
Hamid Moradkhani
Center for Complex Hydrosystems Research, Department of Civil, Construction and Environmental Engineering, University of Alabama

Abstract

In the past few decades, sea level rise (SLR) has been used as one of the most reliable proxies for evincing climate change impacts and significantly contributed to elevated coastal high-water levels around the globe. High tide flooding (HTF) has become more frequent along the U.S. coasts, and it is expected to become more frequent in the following decades. Thus, having an improved estimate of SLR along the coast is crucial for flood hazard mitigation and adaptation planning. There is a lack of a comprehensive framework that provides SLR and HTF flooding statistics at a reasonable spatial resolution that complements current point-based (tide gauge) estimations. To fill this gap, we developed a machine learning algorithm to extract the spatially distributed SLR and HTF thresholds using inputs from observational data. The outcome of this physics-informed machine learning methodology is SLR and HTF estimates under projected SLR by the mid-21st century Background
14 Feb 2023Submitted to ESS Open Archive
15 Feb 2023Published in ESS Open Archive