41 citations as recorded by crossref.

1. Assessing the accuracy of Sentinel-2 instantaneous subpixel shorelines using synchronous UAV ground truth surveys N. Pucino et al. 10.1016/j.rse.2022.113293
2. A physics-informed machine learning model for time-dependent wave runup prediction S. Saviz Naeini & R. Snaiki 10.1016/j.oceaneng.2024.116986
3. On the runup parameterisation for reef-lined coasts G. Franklin & A. Torres-Freyermuth 10.1016/j.ocemod.2021.101929
4. Implications of sea-level rise for overwash enhancement at South Portugal Ó. Ferreira et al. 10.1007/s11069-021-04917-0
5. Machine Learning in Geosciences: A Review of Complex Environmental Monitoring Applications M. Binetti et al. 10.3390/make6020059
6. Wave condition prediction and uncertainty quantification based on SG-MCMC and deep learning model M. Yu et al. 10.1016/j.ocemod.2025.102547
7. A machine learning approach to predicting equilibrium ripple wavelength R. Phillip et al. 10.1016/j.envsoft.2022.105509
8. Coastal Image Classification and Pattern Recognition: Tairua Beach, New Zealand B. Liu et al. 10.3390/s21217352
9. A storm hazard matrix combining coastal flooding and beach erosion C. Leaman et al. 10.1016/j.coastaleng.2021.104001
10. Machine Learning Contribution for a Sustainable Management of the Marine and Coastal Environments: A Critical Review A. Pourzangbar et al. 10.2139/ssrn.4463562
11. Spatial Variation in Coastal Dune Evolution in a High Tidal Range Environment I. Fairley et al. 10.3390/rs12223689
12. Beach State Recognition Using Argus Imagery and Convolutional Neural Networks A. Ellenson et al. 10.3390/rs12233953
13. Observations of wave run-up affected by dune scarp during storm conditions: a two dimensional large-scaled movable bed experiment E. Lee et al. 10.3389/fmars.2024.1369418
14. Bayesian geomorphology O. Korup 10.1002/esp.4995
15. Wave Run-Up Distance Prediction Combined Data-Driven Method and Physical Experiments P. Qin et al. 10.3390/jmse13071298
16. A multi-model ensemble approach to coastal storm erosion prediction J. Simmons & K. Splinter 10.1016/j.envsoft.2022.105356
17. A nearshore evolution model for sandy coasts: IH-LANSloc M. Álvarez-Cuesta et al. 10.1016/j.envsoft.2023.105827
18. Coastal Flooding Hazards in Northern Portugal: A Practical Large-Scale Evaluation of Total Water Levels and Swash Regimes J. Carneiro-Barros et al. 10.3390/w17101478
19. Uncertainty in runup predictions on natural beaches using XBeach nonhydrostatic J. Rutten et al. 10.1016/j.coastaleng.2021.103869
20. On the prediction of runup, setup and swash on beaches P. Gomes da Silva et al. 10.1016/j.earscirev.2020.103148
21. Data-driven shoreline modelling at timescales of days to years J. Simmons & K. Splinter 10.1016/j.coastaleng.2024.104685
22. Hotspot dune erosion on an intermediate beach N. Cohn et al. 10.1016/j.coastaleng.2021.103998
23. 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 10.9765/KSCOE.2022.34.6.315
24. Wave runup and total water level observations from time series imagery at several sites with varying nearshore morphologies M. Buckley et al. 10.1016/j.coastaleng.2024.104600
25. Quaternary and Pliocene sea-level changes at Camarones, central Patagonia, Argentina K. Rubio-Sandoval et al. 10.1016/j.quascirev.2024.108999
26. Ensemble Neural Networks for the Development of Storm Surge Flood Modeling: A Comprehensive Review S. Nezhad et al. 10.3390/jmse11112154
27. Emulator For Eroded Beach And Dune Profiles Due To Storms A. Gharagozlou et al. 10.1029/2022JF006620
28. Machine Learning in Coastal Engineering: Applications, Challenges, and Perspectives M. Abouhalima et al. 10.3390/jmse12040638
29. A framework for national-scale coastal storm hazards early warning I. Turner et al. 10.1016/j.coastaleng.2024.104571
30. Developing a decision tree model to forecast runup and assess uncertainty in empirical formulations M. Itzkin et al. 10.1016/j.coastaleng.2024.104641
31. The Field Geomorphologist in a Time of Artificial Intelligence and Machine Learning C. Houser et al. 10.1080/24694452.2021.1985956
32. A comparative analysis of machine learning algorithms for predicting wave runup A. Durap 10.1007/s44218-023-00033-7
33. Machine learning application in modelling marine and coastal phenomena: a critical review A. Pourzangbar et al. 10.3389/fenve.2023.1235557
34. Gaussian process regression approach for predicting wave attenuation through rigid vegetation K. Ions et al. 10.1016/j.apor.2024.103935
35. A Multidimensional Analysis Approach Toward Sea Cliff Erosion Forecasting M. Krivova et al. 10.3390/rs17050815
36. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery D. Buscombe et al. 10.1029/2021EA002085
37. Automated Extraction of a Depth-Defined Wave Runup Time Series From Lidar Data Using Deep Learning A. Collins et al. 10.1109/TGRS.2023.3244488
38. Prediction of wave runup on beaches using interpretable machine learning T. Kim & W. Lee 10.1016/j.oceaneng.2024.116918
39. Machine-Learning Predictions for Total Water Levels on a Sandy Beach M. Vicens-Miquel et al. 10.2112/JCOASTRES-D-24-00016.1
40. Combining process-based and data-driven approaches to forecast beach and dune change M. Itzkin et al. 10.1016/j.envsoft.2022.105404
41. Comparative analysis of machine learning and deep learning methods for coastal erosion susceptibility mapping T. Van Phong et al. 10.1007/s12145-024-01587-x