21 citations as recorded by crossref.

1. Climate change in a Point-Over-Threshold model: an example on ocean-wave-storm hazard in NE Spain R. Tolosana-Delgado et al. https://doi.org/10.5194/adgeo-26-113-2010
2. Coastal zone significant wave height prediction by supervised machine learning classification algorithms D. Demetriou et al. https://doi.org/10.1016/j.oceaneng.2021.108592
3. REDUCING UNCERTAINTY IN EXTREME WAVES AND STORM SURGES USING A COMBINED EXTREME VALUE MODEL AND WAVELETS P. Galiatsatou & P. Prinos https://doi.org/10.9753/icce.v33.management.6
4. Low-frequency variability of storms in the northern Black Sea and associated processes in the ocean–atmosphere system A. Polonsky et al. https://doi.org/10.1007/s10113-013-0546-z
5. Spatial Bayesian hierarchical modelling of extreme sea states C. Clancy et al. https://doi.org/10.1016/j.ocemod.2016.09.015
6. Predicting surf zone injuries along the Delaware coast using a Bayesian network M. Doelp et al. https://doi.org/10.1007/s11069-019-03697-y
7. Nowcasting significant wave height by hierarchical machine learning classification D. Demetriou et al. https://doi.org/10.1016/j.oceaneng.2021.110130
8. The XIOM: 20 years of a regional coastal observatory in the Spanish Catalan coast R. Bolaños et al. https://doi.org/10.1016/j.jmarsys.2007.12.018
9. Extremes from scarce data: The role of Bayesian and scaling techniques in reducing uncertainty A. Sánchez-Arcilla et al. https://doi.org/10.1080/00221686.2008.9521956
10. Uncertainty analysis on extreme value analysis of significant wave height at eastern coast of Korea S. Kim et al. https://doi.org/10.1016/j.apor.2013.02.001
11. Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction I. Malekmohamadi et al. https://doi.org/10.1016/j.oceaneng.2010.11.020
12. A two-step framework for over-threshold modelling of environmental extremes P. Bernardara et al. https://doi.org/10.5194/nhess-14-635-2014
13. Wave Height Prediction Using Artificial Immune Recognition Systems (AIRS) and Some Other Data Mining Techniques M. Nikoo & R. Kerachian https://doi.org/10.1007/s40996-017-0067-y
14. Bayesian trend analysis of extreme wind using observed and hindcast series off the Catalan coast, NW Mediterranean Sea M. Ortego et al. https://doi.org/10.5194/nhess-14-2387-2014
15. Modeling non-stationary extreme waves using a point process approach and wavelets P. Galiatsatou & P. Prinos https://doi.org/10.1007/s00477-010-0448-2
16. Clustering Time Series of Sea Levels: Extreme Value Approach M. Scotto et al. https://doi.org/10.1061/(ASCE)WW.1943-5460.0000045
17. Wave height prediction using the rough set theory A. Abed-Elmdoust & R. Kerachian https://doi.org/10.1016/j.oceaneng.2012.07.020
18. Bayesian inference for long-term prediction of significant wave height M. Scotto & C. Guedes Soares https://doi.org/10.1016/j.coastaleng.2006.11.003
19. Long-term time-dependent stochastic modelling of extreme waves E. Vanem https://doi.org/10.1007/s00477-010-0431-y
20. Wave height data assimilation using non-stationary kriging R. Tolosana-Delgado et al. https://doi.org/10.1016/j.cageo.2010.05.019
21. Coastal Adaptation and Uncertainties: The Need of Ethics for a Shared Coastal Future A. Sánchez-Arcilla et al. https://doi.org/10.3389/fmars.2021.717781