Articles | Volume 19, issue 10
https://doi.org/10.5194/nhess-19-2295-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-19-2295-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
22 Oct 2019
Research article | Highlight paper |
| 22 Oct 2019
| 22 Oct 2019
Ensemble models from machine learning: an example of wave runup and coastal dune erosion
Tomas Beuzen
CORRESPONDING AUTHOR
Water Research Laboratory, School of Civil and Environmental
Engineering, UNSW Sydney, Sydney, NSW, Australia
Evan B. Goldstein
Department of Geography, Environment, and Sustainability, University of North Carolina at Greensboro, Greensboro, NC, USA
Kristen D. Splinter
Water Research Laboratory, School of Civil and Environmental
Engineering, UNSW Sydney, Sydney, NSW, Australia
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Cited
46 citations as recorded by crossref.
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- On the runup parameterisation for reef-lined coasts G. Franklin & A. Torres-Freyermuth https://doi.org/10.1016/j.ocemod.2021.101929
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- Coastal Image Classification and Pattern Recognition: Tairua Beach, New Zealand B. Liu et al. https://doi.org/10.3390/s21217352
- A storm hazard matrix combining coastal flooding and beach erosion C. Leaman et al. https://doi.org/10.1016/j.coastaleng.2021.104001
- Bayesian geomorphology O. Korup https://doi.org/10.1002/esp.4995
- Coastal Flooding Hazards in Northern Portugal: A Practical Large-Scale Evaluation of Total Water Levels and Swash Regimes J. Carneiro-Barros et al. https://doi.org/10.3390/w17101478
- Uncertainty in runup predictions on natural beaches using XBeach nonhydrostatic J. Rutten et al. https://doi.org/10.1016/j.coastaleng.2021.103869
- Data-driven shoreline modelling at timescales of days to years J. Simmons & K. Splinter https://doi.org/10.1016/j.coastaleng.2024.104685
- Advances and challenges in predicting wave runup in coastal regions: A scoping review of empirical, numerical, and AI-based approaches E. Amini et al. https://doi.org/10.1016/j.earscirev.2026.105399
- Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review S. Nezhad et al. https://doi.org/10.3390/jmse11112154
- A comparative analysis of machine learning algorithms for predicting wave runup A. Durap https://doi.org/10.1007/s44218-023-00033-7
- Machine learning application in modelling marine and coastal phenomena: a critical review A. Pourzangbar et al. https://doi.org/10.3389/fenve.2023.1235557
- A Multidimensional Analysis Approach Toward Sea Cliff Erosion Forecasting M. Krivova et al. https://doi.org/10.3390/rs17050815
- Human‐in‐the‐Loop Segmentation of Earth Surface Imagery D. Buscombe et al. https://doi.org/10.1029/2021EA002085
- Prediction of wave runup on beaches using interpretable machine learning T. Kim & W. Lee https://doi.org/10.1016/j.oceaneng.2024.116918
- Assessing the accuracy of Sentinel-2 instantaneous subpixel shorelines using synchronous UAV ground truth surveys N. Pucino et al. https://doi.org/10.1016/j.rse.2022.113293
- MARS modelling for spatial analysis of coastal erosion susceptibility G. Azzara et al. https://doi.org/10.1016/j.geomorph.2026.110240
- Rising impact hours on sandy beaches linked to total water level variability along U.S. coastlines G. Quadrado & K. Serafin https://doi.org/10.1016/j.coastaleng.2026.104963
- Implications of sea-level rise for overwash enhancement at South Portugal Ó. Ferreira et al. https://doi.org/10.1007/s11069-021-04917-0
- Machine Learning in Geosciences: A Review of Complex Environmental Monitoring Applications M. Binetti et al. https://doi.org/10.3390/make6020059
- A Review of Assessment Methods for Coastal Hydro-Environmental Processes: Research Trends and Challenges Q. Lee et al. https://doi.org/10.3390/w17223278
- Machine Learning Contribution for a Sustainable Management of the Marine and Coastal Environments: A Critical Review A. Pourzangbar et al. https://doi.org/10.2139/ssrn.4463562
- Spatial Variation in Coastal Dune Evolution in a High Tidal Range Environment I. Fairley et al. https://doi.org/10.3390/rs12223689
- Beach State Recognition Using Argus Imagery and Convolutional Neural Networks A. Ellenson et al. https://doi.org/10.3390/rs12233953
- Observations of wave run-up affected by dune scarp during storm conditions: a two dimensional large-scaled movable bed experiment E. Lee et al. https://doi.org/10.3389/fmars.2024.1369418
- Wave Run-Up Distance Prediction Combined Data-Driven Method and Physical Experiments P. Qin et al. https://doi.org/10.3390/jmse13071298
- A multi-model ensemble approach to coastal storm erosion prediction J. Simmons & K. Splinter https://doi.org/10.1016/j.envsoft.2022.105356
- A nearshore evolution model for sandy coasts: IH-LANSloc M. Álvarez-Cuesta et al. https://doi.org/10.1016/j.envsoft.2023.105827
- On the prediction of runup, setup and swash on beaches P. Gomes da Silva et al. https://doi.org/10.1016/j.earscirev.2020.103148
- Hotspot dune erosion on an intermediate beach N. Cohn et al. https://doi.org/10.1016/j.coastaleng.2021.103998
- Estimation of the Input Wave Height of the Wave Generator for Regular Waves by Using Artificial Neural Networks and Gaussian Process Regression J. Oh & S. Oh https://doi.org/10.9765/KSCOE.2022.34.6.315
- Wave runup and total water level observations from time series imagery at several sites with varying nearshore morphologies M. Buckley et al. https://doi.org/10.1016/j.coastaleng.2024.104600
- Quaternary and Pliocene sea-level changes at Camarones, central Patagonia, Argentina K. Rubio-Sandoval et al. https://doi.org/10.1016/j.quascirev.2024.108999
- The 3rd International Workshop on swash-zone processes A. Schueller et al. https://doi.org/10.1016/j.coastaleng.2025.104891
- Emulator For Eroded Beach And Dune Profiles Due To Storms A. Gharagozlou et al. https://doi.org/10.1029/2022JF006620
- Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives M. Abouhalima et al. https://doi.org/10.3390/jmse12040638
- A framework for national-scale coastal storm hazards early warning I. Turner et al. https://doi.org/10.1016/j.coastaleng.2024.104571
- Developing a decision tree model to forecast runup and assess uncertainty in empirical formulations M. Itzkin et al. https://doi.org/10.1016/j.coastaleng.2024.104641
- The Field Geomorphologist in a Time of Artificial Intelligence and Machine Learning C. Houser et al. https://doi.org/10.1080/24694452.2021.1985956
- Gaussian process regression approach for predicting wave attenuation through rigid vegetation K. Ions et al. https://doi.org/10.1016/j.apor.2024.103935
- Automated Extraction of a Depth-Defined Wave Runup Time Series From Lidar Data Using Deep Learning A. Collins et al. https://doi.org/10.1109/TGRS.2023.3244488
- Machine-Learning Predictions for Total Water Levels on a Sandy Beach M. Vicens-Miquel et al. https://doi.org/10.2112/JCOASTRES-D-24-00016.1
- Combining process-based and data-driven approaches to forecast beach and dune change M. Itzkin et al. https://doi.org/10.1016/j.envsoft.2022.105404
- Comparative analysis of machine learning and deep learning methods for coastal erosion susceptibility mapping T. Van Phong et al. https://doi.org/10.1007/s12145-024-01587-x
46 citations as recorded by crossref.
- A physics-informed machine learning model for time-dependent wave runup prediction S. Saviz Naeini & R. Snaiki https://doi.org/10.1016/j.oceaneng.2024.116986
- On the runup parameterisation for reef-lined coasts G. Franklin & A. Torres-Freyermuth https://doi.org/10.1016/j.ocemod.2021.101929
- Wave condition prediction and uncertainty quantification based on SG-MCMC and deep learning model M. Yu et al. https://doi.org/10.1016/j.ocemod.2025.102547
- A machine learning approach to predicting equilibrium ripple wavelength R. Phillip et al. https://doi.org/10.1016/j.envsoft.2022.105509
- Coastal Image Classification and Pattern Recognition: Tairua Beach, New Zealand B. Liu et al. https://doi.org/10.3390/s21217352
- A storm hazard matrix combining coastal flooding and beach erosion C. Leaman et al. https://doi.org/10.1016/j.coastaleng.2021.104001
- Bayesian geomorphology O. Korup https://doi.org/10.1002/esp.4995
- Coastal Flooding Hazards in Northern Portugal: A Practical Large-Scale Evaluation of Total Water Levels and Swash Regimes J. Carneiro-Barros et al. https://doi.org/10.3390/w17101478
- Uncertainty in runup predictions on natural beaches using XBeach nonhydrostatic J. Rutten et al. https://doi.org/10.1016/j.coastaleng.2021.103869
- Data-driven shoreline modelling at timescales of days to years J. Simmons & K. Splinter https://doi.org/10.1016/j.coastaleng.2024.104685
- Advances and challenges in predicting wave runup in coastal regions: A scoping review of empirical, numerical, and AI-based approaches E. Amini et al. https://doi.org/10.1016/j.earscirev.2026.105399
- Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review S. Nezhad et al. https://doi.org/10.3390/jmse11112154
- A comparative analysis of machine learning algorithms for predicting wave runup A. Durap https://doi.org/10.1007/s44218-023-00033-7
- Machine learning application in modelling marine and coastal phenomena: a critical review A. Pourzangbar et al. https://doi.org/10.3389/fenve.2023.1235557
- A Multidimensional Analysis Approach Toward Sea Cliff Erosion Forecasting M. Krivova et al. https://doi.org/10.3390/rs17050815
- Human‐in‐the‐Loop Segmentation of Earth Surface Imagery D. Buscombe et al. https://doi.org/10.1029/2021EA002085
- Prediction of wave runup on beaches using interpretable machine learning T. Kim & W. Lee https://doi.org/10.1016/j.oceaneng.2024.116918
- Assessing the accuracy of Sentinel-2 instantaneous subpixel shorelines using synchronous UAV ground truth surveys N. Pucino et al. https://doi.org/10.1016/j.rse.2022.113293
- MARS modelling for spatial analysis of coastal erosion susceptibility G. Azzara et al. https://doi.org/10.1016/j.geomorph.2026.110240
- Rising impact hours on sandy beaches linked to total water level variability along U.S. coastlines G. Quadrado & K. Serafin https://doi.org/10.1016/j.coastaleng.2026.104963
- Implications of sea-level rise for overwash enhancement at South Portugal Ó. Ferreira et al. https://doi.org/10.1007/s11069-021-04917-0
- Machine Learning in Geosciences: A Review of Complex Environmental Monitoring Applications M. Binetti et al. https://doi.org/10.3390/make6020059
- A Review of Assessment Methods for Coastal Hydro-Environmental Processes: Research Trends and Challenges Q. Lee et al. https://doi.org/10.3390/w17223278
- Machine Learning Contribution for a Sustainable Management of the Marine and Coastal Environments: A Critical Review A. Pourzangbar et al. https://doi.org/10.2139/ssrn.4463562
- Spatial Variation in Coastal Dune Evolution in a High Tidal Range Environment I. Fairley et al. https://doi.org/10.3390/rs12223689
- Beach State Recognition Using Argus Imagery and Convolutional Neural Networks A. Ellenson et al. https://doi.org/10.3390/rs12233953
- Observations of wave run-up affected by dune scarp during storm conditions: a two dimensional large-scaled movable bed experiment E. Lee et al. https://doi.org/10.3389/fmars.2024.1369418
- Wave Run-Up Distance Prediction Combined Data-Driven Method and Physical Experiments P. Qin et al. https://doi.org/10.3390/jmse13071298
- A multi-model ensemble approach to coastal storm erosion prediction J. Simmons & K. Splinter https://doi.org/10.1016/j.envsoft.2022.105356
- A nearshore evolution model for sandy coasts: IH-LANSloc M. Álvarez-Cuesta et al. https://doi.org/10.1016/j.envsoft.2023.105827
- On the prediction of runup, setup and swash on beaches P. Gomes da Silva et al. https://doi.org/10.1016/j.earscirev.2020.103148
- Hotspot dune erosion on an intermediate beach N. Cohn et al. https://doi.org/10.1016/j.coastaleng.2021.103998
- Estimation of the Input Wave Height of the Wave Generator for Regular Waves by Using Artificial Neural Networks and Gaussian Process Regression J. Oh & S. Oh https://doi.org/10.9765/KSCOE.2022.34.6.315
- Wave runup and total water level observations from time series imagery at several sites with varying nearshore morphologies M. Buckley et al. https://doi.org/10.1016/j.coastaleng.2024.104600
- Quaternary and Pliocene sea-level changes at Camarones, central Patagonia, Argentina K. Rubio-Sandoval et al. https://doi.org/10.1016/j.quascirev.2024.108999
- The 3rd International Workshop on swash-zone processes A. Schueller et al. https://doi.org/10.1016/j.coastaleng.2025.104891
- Emulator For Eroded Beach And Dune Profiles Due To Storms A. Gharagozlou et al. https://doi.org/10.1029/2022JF006620
- Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives M. Abouhalima et al. https://doi.org/10.3390/jmse12040638
- A framework for national-scale coastal storm hazards early warning I. Turner et al. https://doi.org/10.1016/j.coastaleng.2024.104571
- Developing a decision tree model to forecast runup and assess uncertainty in empirical formulations M. Itzkin et al. https://doi.org/10.1016/j.coastaleng.2024.104641
- The Field Geomorphologist in a Time of Artificial Intelligence and Machine Learning C. Houser et al. https://doi.org/10.1080/24694452.2021.1985956
- Gaussian process regression approach for predicting wave attenuation through rigid vegetation K. Ions et al. https://doi.org/10.1016/j.apor.2024.103935
- Automated Extraction of a Depth-Defined Wave Runup Time Series From Lidar Data Using Deep Learning A. Collins et al. https://doi.org/10.1109/TGRS.2023.3244488
- Machine-Learning Predictions for Total Water Levels on a Sandy Beach M. Vicens-Miquel et al. https://doi.org/10.2112/JCOASTRES-D-24-00016.1
- Combining process-based and data-driven approaches to forecast beach and dune change M. Itzkin et al. https://doi.org/10.1016/j.envsoft.2022.105404
- Comparative analysis of machine learning and deep learning methods for coastal erosion susceptibility mapping T. Van Phong et al. https://doi.org/10.1007/s12145-024-01587-x
Saved (final revised paper)
Latest update: 23 Jun 2026
Short summary
Wave runup is important for characterizing coastal vulnerability to wave action; however, it is complex and uncertain to predict. We use machine learning with a high-resolution dataset of wave runup to develop an accurate runup predictor that includes prediction uncertainty. We show how uncertainty in wave runup predictions can be used practically in a model of dune erosion to make ensemble predictions that provide more information and greater predictive skill than a single deterministic model.
Wave runup is important for characterizing coastal vulnerability to wave action; however, it is...
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