Articles | Volume 24, issue 10
https://doi.org/10.5194/nhess-24-3537-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-24-3537-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Oct 2024
Research article |
| 17 Oct 2024
| 17 Oct 2024
Transferability of machine-learning-based modeling frameworks across flood events for hindcasting maximum river water depths in coastal watersheds
Maryam Pakdehi
Department of Civil and Environmental Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA
Resilient Infrastructure and Disaster Response Center, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA
Ebrahim Ahmadisharaf
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA
Resilient Infrastructure and Disaster Response Center, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA
Behzad Nazari
Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 76010, USA
Eunsaem Cho
Department of Civil and Environmental Engineering, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA
Resilient Infrastructure and Disaster Response Center, FAMU-FSU College of Engineering, Tallahassee, FL 32310, USA
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Cited
17 citations as recorded by crossref.
- Fusion of data-driven models with a knowledge-guided loss function for flood forecasting H. Malik et al. https://doi.org/10.1016/j.asoc.2025.113742
- A unified subregional framework for modeling stream water quality across watersheds of a hydrologic subregion I. Adedeji et al. https://doi.org/10.1016/j.scitotenv.2024.177870
- Interpretable boosting algorithms for prediction and data imputation of River Sediment M. Zounemat-Kermani & M. Kheimi https://doi.org/10.1016/j.asoc.2025.114378
- Comparing strategies for training LSTM models for street-scale urban flood prediction in Norfolk, Virginia J. Jeong et al. https://doi.org/10.1016/j.ejrh.2026.103405
- Impacts of major floods on new human respiratory health symptoms in indoor environments M. Pakdehi et al. https://doi.org/10.1016/j.jclepro.2026.148247
- Learning to filter: snow data assimilation using a Long Short-Term Memory network G. Blandini et al. https://doi.org/10.5194/tc-19-4759-2025
- Deep Learning–Based Multiobjective Optimization Framework for Stormwater Management Models J. Li et al. https://doi.org/10.1061/JHYEFF.HEENG-6784
- Transfer learning for identifying rainwater harvesting sites in training data-scarce catchments S. Kommula et al. https://doi.org/10.1038/s41598-026-51218-2
- Accelerating tropical cyclone wave height estimation via machine learning and deep latent surrogates T. Du et al. https://doi.org/10.1016/j.oceaneng.2026.124560
- Transferability of machine/deep learning-based prediction of fluvial flood extent to distinct river sections in Slovakia based on benchmark flood maps and high-resolution spatial data M. Vojtek et al. https://doi.org/10.1016/j.ejrh.2026.103339
- PyFlood: Rapid high-resolution coastal flood mapping with digital elevation model, land cover and water level data A. Santos Cruz et al. https://doi.org/10.1016/j.envsoft.2026.107010
- Coupling computational fluid dynamics with Decision Tree and k-Nearest Neighbours classifications for stagnation and water quality assessment in arid rivers: evidence from the Tigris M. Hathal et al. https://doi.org/10.1007/s13201-026-02883-1
- Assessing the impact of land use and land cover on predicting in-stream total phosphorus using GIS and machine learning models H. Lee et al. https://doi.org/10.1016/j.ecoinf.2026.103598
- A multimodal full-cycle risk perception and early warning model for mine water inrush disasters J. Shi et al. https://doi.org/10.1016/j.ress.2026.112362
- Preface: Hydro-meteorological extremes and hazards: vulnerability, risk, impacts, and mitigation F. Marra et al. https://doi.org/10.5194/nhess-25-2565-2025
- Incorporating artificial intelligence into the future of stormwater management M. Rahman et al. https://doi.org/10.1007/s42452-026-08488-2
- Fluvial flood extent modeling using machine learning algorithms trained on benchmark flood maps: new insights for computationally efficient flood mapping M. Vojtek et al. https://doi.org/10.1016/j.watcyc.2026.04.002
17 citations as recorded by crossref.
- Fusion of data-driven models with a knowledge-guided loss function for flood forecasting H. Malik et al. https://doi.org/10.1016/j.asoc.2025.113742
- A unified subregional framework for modeling stream water quality across watersheds of a hydrologic subregion I. Adedeji et al. https://doi.org/10.1016/j.scitotenv.2024.177870
- Interpretable boosting algorithms for prediction and data imputation of River Sediment M. Zounemat-Kermani & M. Kheimi https://doi.org/10.1016/j.asoc.2025.114378
- Comparing strategies for training LSTM models for street-scale urban flood prediction in Norfolk, Virginia J. Jeong et al. https://doi.org/10.1016/j.ejrh.2026.103405
- Impacts of major floods on new human respiratory health symptoms in indoor environments M. Pakdehi et al. https://doi.org/10.1016/j.jclepro.2026.148247
- Learning to filter: snow data assimilation using a Long Short-Term Memory network G. Blandini et al. https://doi.org/10.5194/tc-19-4759-2025
- Deep Learning–Based Multiobjective Optimization Framework for Stormwater Management Models J. Li et al. https://doi.org/10.1061/JHYEFF.HEENG-6784
- Transfer learning for identifying rainwater harvesting sites in training data-scarce catchments S. Kommula et al. https://doi.org/10.1038/s41598-026-51218-2
- Accelerating tropical cyclone wave height estimation via machine learning and deep latent surrogates T. Du et al. https://doi.org/10.1016/j.oceaneng.2026.124560
- Transferability of machine/deep learning-based prediction of fluvial flood extent to distinct river sections in Slovakia based on benchmark flood maps and high-resolution spatial data M. Vojtek et al. https://doi.org/10.1016/j.ejrh.2026.103339
- PyFlood: Rapid high-resolution coastal flood mapping with digital elevation model, land cover and water level data A. Santos Cruz et al. https://doi.org/10.1016/j.envsoft.2026.107010
- Coupling computational fluid dynamics with Decision Tree and k-Nearest Neighbours classifications for stagnation and water quality assessment in arid rivers: evidence from the Tigris M. Hathal et al. https://doi.org/10.1007/s13201-026-02883-1
- Assessing the impact of land use and land cover on predicting in-stream total phosphorus using GIS and machine learning models H. Lee et al. https://doi.org/10.1016/j.ecoinf.2026.103598
- A multimodal full-cycle risk perception and early warning model for mine water inrush disasters J. Shi et al. https://doi.org/10.1016/j.ress.2026.112362
- Preface: Hydro-meteorological extremes and hazards: vulnerability, risk, impacts, and mitigation F. Marra et al. https://doi.org/10.5194/nhess-25-2565-2025
- Incorporating artificial intelligence into the future of stormwater management M. Rahman et al. https://doi.org/10.1007/s42452-026-08488-2
- Fluvial flood extent modeling using machine learning algorithms trained on benchmark flood maps: new insights for computationally efficient flood mapping M. Vojtek et al. https://doi.org/10.1016/j.watcyc.2026.04.002
Saved (final revised paper)
Latest update: 19 Jul 2026
Short summary
Machine learning (ML) algorithms have increasingly received attention for modeling flood events. However, there are concerns about the transferability of these models (their capability in predicting out-of-sample and unseen events). Here, we show that ML models can be transferable for hindcasting maximum river flood depths across extreme events (four hurricanes) in a large coastal watershed (HUC6) when informed by the spatial distribution of pertinent features and underlying physical processes.
Machine learning (ML) algorithms have increasingly received attention for modeling flood events....
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