Predicting the Evolution of Extreme Water Levels With Long Short‐Term Memory Station‐Based Approximated Models and Transfer Learning Techniques

Predicting the Evolution of Extreme Water Levels With Long Short‐Term Memory Station‐Based Approximated Models and Transfer Learning Techniques

Abstract Extreme water levels (EWLs) resulting from cyclones pose significant flood hazards and risks to coastal communities and interconnected ecosystems. To date, physically based models have enabled accurate prediction of EWLs despite their inherent high computational cost. However, the applicabi...

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Bibliographic Details
Main Authors: Samuel Daramola, David F. Muñoz, Paul Muñoz, Siddharth Saksena, Jennifer Irish
Format: Article
Language:English
Published: Wiley 2025-03-01
Series:Water Resources Research
Subjects:
long short‐term memory networks
transfer learning
extreme water level
cyclones
attention layer
Online Access:https://doi.org/10.1029/2024WR039054
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https://doi.org/10.1029/2024WR039054

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