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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| Format: | Article |
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Wiley
2025-03-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR039054 |
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| author | Samuel Daramola David F. Muñoz Paul Muñoz Siddharth Saksena Jennifer Irish |
| author_facet | Samuel Daramola David F. Muñoz Paul Muñoz Siddharth Saksena Jennifer Irish |
| author_sort | Samuel Daramola |
| collection | DOAJ |
| description | 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 applicability of these models is limited to data‐rich sites with diverse characteristics. The dependence on high quality spatiotemporal data, which is often computationally expensive, hinders the applicability of these models to regions of either limited or data‐scarce conditions. To address this challenge, we present a Long Short‐Term Memory (LSTM) network framework to predict the evolution of EWLs beyond site‐specific training stations. The framework, named LSTM‐Station Approximated Models (LSTM‐SAM), consists of a collection of bidirectional LSTM models enhanced with a custom attention mechanism layer embedded in the architecture. LSTM‐SAM incorporates a transfer learning approach applicable to target (tide‐gage) stations along the U.S. Atlantic Coast. Importantly, LSTM‐SAM helps analyze: (a) the underlying limitations associated with transfer learning, (b) evaluate EWL predictions beyond training domains, and (c) capture the evolution of EWL caused by tropical and extratropical cyclones. The framework demonstrates satisfactory performance with “transferable” models achieving Kling‐Gupta Efficiency (KGE), Nash‐Sutcliffe Efficiency (NSE), and Root‐Mean Square Error (RMSE) ranging from 0.78 to 0.92, 0.90 to 0.97, and 0.09–0.18 m at the target stations, respectively. We show that LSTM‐SAM can accurately predict not only EWLs but also their evolution over time, that is, onset, peak, and dissipation, which could assist in operational flood forecasting in regions with limited resources to set up physically based models. |
| format | Article |
| id | doaj-art-d02e27716f1b4eb48a8d8f99529c090f |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-d02e27716f1b4eb48a8d8f99529c090f2025-08-20T03:30:57ZengWileyWater Resources Research0043-13971944-79732025-03-01613n/an/a10.1029/2024WR039054Predicting the Evolution of Extreme Water Levels With Long Short‐Term Memory Station‐Based Approximated Models and Transfer Learning TechniquesSamuel Daramola0David F. Muñoz1Paul Muñoz2Siddharth Saksena3Jennifer Irish4Department of Civil and Environmental Engineering Virginia Tech Blacksburg VA USADepartment of Civil and Environmental Engineering Virginia Tech Blacksburg VA USADepartment of Water and Climate Vrije Universiteit Brussel (VUB) Brussels VA USADepartment of Civil and Environmental Engineering Virginia Tech Blacksburg VA USADepartment of Civil and Environmental Engineering Virginia Tech Blacksburg VA USAAbstract 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 applicability of these models is limited to data‐rich sites with diverse characteristics. The dependence on high quality spatiotemporal data, which is often computationally expensive, hinders the applicability of these models to regions of either limited or data‐scarce conditions. To address this challenge, we present a Long Short‐Term Memory (LSTM) network framework to predict the evolution of EWLs beyond site‐specific training stations. The framework, named LSTM‐Station Approximated Models (LSTM‐SAM), consists of a collection of bidirectional LSTM models enhanced with a custom attention mechanism layer embedded in the architecture. LSTM‐SAM incorporates a transfer learning approach applicable to target (tide‐gage) stations along the U.S. Atlantic Coast. Importantly, LSTM‐SAM helps analyze: (a) the underlying limitations associated with transfer learning, (b) evaluate EWL predictions beyond training domains, and (c) capture the evolution of EWL caused by tropical and extratropical cyclones. The framework demonstrates satisfactory performance with “transferable” models achieving Kling‐Gupta Efficiency (KGE), Nash‐Sutcliffe Efficiency (NSE), and Root‐Mean Square Error (RMSE) ranging from 0.78 to 0.92, 0.90 to 0.97, and 0.09–0.18 m at the target stations, respectively. We show that LSTM‐SAM can accurately predict not only EWLs but also their evolution over time, that is, onset, peak, and dissipation, which could assist in operational flood forecasting in regions with limited resources to set up physically based models.https://doi.org/10.1029/2024WR039054long short‐term memory networkstransfer learningextreme water levelcyclonesattention layer |
| spellingShingle | Samuel Daramola David F. Muñoz Paul Muñoz Siddharth Saksena Jennifer Irish Predicting the Evolution of Extreme Water Levels With Long Short‐Term Memory Station‐Based Approximated Models and Transfer Learning Techniques Water Resources Research long short‐term memory networks transfer learning extreme water level cyclones attention layer |
| title | Predicting the Evolution of Extreme Water Levels With Long Short‐Term Memory Station‐Based Approximated Models and Transfer Learning Techniques |
| title_full | Predicting the Evolution of Extreme Water Levels With Long Short‐Term Memory Station‐Based Approximated Models and Transfer Learning Techniques |
| title_fullStr | Predicting the Evolution of Extreme Water Levels With Long Short‐Term Memory Station‐Based Approximated Models and Transfer Learning Techniques |
| title_full_unstemmed | Predicting the Evolution of Extreme Water Levels With Long Short‐Term Memory Station‐Based Approximated Models and Transfer Learning Techniques |
| title_short | Predicting the Evolution of Extreme Water Levels With Long Short‐Term Memory Station‐Based Approximated Models and Transfer Learning Techniques |
| title_sort | predicting the evolution of extreme water levels with long short term memory station based approximated models and transfer learning techniques |
| topic | long short‐term memory networks transfer learning extreme water level cyclones attention layer |
| url | https://doi.org/10.1029/2024WR039054 |
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