Abstract
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Kévin Dubois, Marilis Rupert, Erik Nilsson, Anna Rutgersson
Abstract
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Ensemble robust local mean decomposition integrated with random forest for short-term significant wave height forecasting
10.1016/j.renene.2023.01.108 · 2023
Wind waves
2018
An innovative deep learning-based approach for significant wave height forecasting
10.1016/j.oceaneng.2025.120623 · 2025
Using random forests to forecast daily extreme sea level occurrences at the Baltic Coast
10.5194/nhess-25-1139-2025 · 2025
Event-based wave statistics for the Baltic Sea
10.5194/sp-4-osr8-10-2024 · 2024
Swell hindcast statistics for the Baltic Sea
10.5194/os-17-1815-2021 · 2021
Random forests
10.1023/a:1010933404324 · 2001
Using random forest and gradient boosting trees to improve wave forecast at a specific location
10.1016/j.apor.2020.102339 · 2020
Statistical wave climate projections for coastal impact assessments
10.1002/2017ef000609 · 2017
A real-time spatiotemporal machine learning framework for the prediction of nearshore wave conditions
Provenance
crossref
Confidence 100%
ror
Confidence 99%
ror
Confidence 99%
openalex
Confidence 95%
datacite
Confidence 0%
2022
Coastal zone significant wave height prediction by supervised machine learning classification algorithms
10.1016/j.oceaneng.2021.108592 · 2021
Exploring storm tides projections and their return levels around the Baltic Sea using a machine learning approach
2025
Assessing future changes in Baltic sea extreme wave heights using a machine learning approach
10.1007/s00704-025-05758-8 · 2025
Review on deep learning research and applications in wind and wave energy
10.3390/en15041510 · 2022
Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data
10.5194/wes-9-821-2024 · 2024
Generalized machine learning models to predict significant wave height utilizing wind and atmospheric parameters
2024
Advancing wind-waves climate science: the COWCLIP project
10.1175/bams-d-11-00184.1 · 2012
ERA5 hourly data on single levels from 1940 to present
2023
Drivers of high-frequency extreme sea levels around northern Europe – synergies between recurrent neural networks and random forest
10.5194/os-21-1813-2025 · 2025
10.16993/tellusa.3216
10.16993/tellusa.3216
Short-term forecasting of the wave energy flux: analogues, random forests, and physics-based models
10.1016/j.oceaneng.2015.05.038 · 2015
A machine learning framework to forecast wave conditions
10.1016/j.coastaleng.2018.03.004 · 2018
An initial climatology of gales over the North Sea
1977
Review on applications of machine learning in coastal and ocean engineering
10.26748/ksoe.2022.007 · 2022
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Climate change and offshore wind energy in the Baltic Sea
10.1093/acrefore/9780190228620.013.910 · 2024
Understanding the dynamics of Ocean wave-current interactions through multivariate multi-step time series forecasting
10.1080/08839514.2024.2393978 · 2024
Contribution of wave setup to projected coastal Sea level changes
10.1029/2020jc016078 · 2020
A deep learning approach to predict significant wave height using long short-term memory
10.1016/j.ocemod.2022.102151 · 2023
DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin
10.5194/gmd-17-4705-2024 · 2024
A deep learning-based evolutionary model for short-term wind speed forecasting : a case study of the Lillgrund offshore wind farm
10.1016/j.enconman.2021.114002 · 2021
Assessment of extreme and metocean conditions in the Swedish exclusive economic zone for wave energy
10.3390/atmos11030229 · 2020
A deep hybrid network for significant wave height estimation
10.1016/j.ocemod.2024.102363 · 2024
Machine learning application in modelling marine and coastal phenomena : a critical review
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Using land-based stations for air–sea interaction studies
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Prediction of significant wave height; comparison networks, extreme learning and support vector machine learning models of artificial neural between nested grid numerical model, and machines
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Application of the LSTM models for Baltic Sea wave spectra estimation
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Baltic sea wave climate in 1979 – 2018 : numerical modelling results
10.1016/j.oceaneng.2024.117088 · 2024
Extremes and decadal variations in the Baltic Sea wave conditions
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Numerical simulations of wave climate in the Baltic Sea: a review
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Spatial-temporal wave height forecast using deep learning and public reanalysis dataset
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Ocean wave forecasting with deep learning as alternative to conventional models
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Long-term spatial variations in the Baltic Sea wave fields
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Numerical simulations of wave climate in the Baltic Sea: a review
10.1016/j.oceano.2022.01.004 · doi-reference
Baltic sea wave climate in 1979 – 2018 : numerical modelling results
10.1016/j.oceaneng.2024.117088 · doi-reference
Application of the LSTM models for Baltic Sea wave spectra estimation
10.1109/jstars.2022.3220882 · doi-reference
A deep hybrid network for significant wave height estimation
10.1016/j.ocemod.2024.102363 · doi-reference
Assessment of extreme and metocean conditions in the Swedish exclusive economic zone for wave energy
10.3390/atmos11030229 · doi-reference
A deep learning-based evolutionary model for short-term wind speed forecasting : a case study of the Lillgrund offshore wind farm
10.1016/j.enconman.2021.114002 · doi-reference
DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin
10.5194/gmd-17-4705-2024 · doi-reference
A deep learning approach to predict significant wave height using long short-term memory
10.1016/j.ocemod.2022.102151 · doi-reference
Contribution of wave setup to projected coastal Sea level changes
10.1029/2020jc016078 · doi-reference
Understanding the dynamics of Ocean wave-current interactions through multivariate multi-step time series forecasting
10.1080/08839514.2024.2393978 · doi-reference
Climate change and offshore wind energy in the Baltic Sea
10.1093/acrefore/9780190228620.013.910 · doi-reference
Review on applications of machine learning in coastal and ocean engineering
10.26748/ksoe.2022.007 · doi-reference
A machine learning framework to forecast wave conditions
10.1016/j.coastaleng.2018.03.004 · doi-reference
Short-term forecasting of the wave energy flux: analogues, random forests, and physics-based models
10.1016/j.oceaneng.2015.05.038 · doi-reference
10.16993/tellusa.3216
10.16993/tellusa.3216 · doi-reference
Drivers of high-frequency extreme sea levels around northern Europe – synergies between recurrent neural networks and random forest
10.5194/os-21-1813-2025 · doi-reference
Advancing wind-waves climate science: the COWCLIP project
10.1175/bams-d-11-00184.1 · doi-reference
Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data
10.5194/wes-9-821-2024 · doi-reference
Review on deep learning research and applications in wind and wave energy
10.3390/en15041510 · doi-reference
Assessing future changes in Baltic sea extreme wave heights using a machine learning approach
10.1007/s00704-025-05758-8 · doi-reference
Coastal zone significant wave height prediction by supervised machine learning classification algorithms
10.1016/j.oceaneng.2021.108592 · doi-reference
Statistical wave climate projections for coastal impact assessments
10.1002/2017ef000609 · doi-reference
Using random forest and gradient boosting trees to improve wave forecast at a specific location
10.1016/j.apor.2020.102339 · doi-reference
Random forests
10.1023/a:1010933404324 · doi-reference
Swell hindcast statistics for the Baltic Sea
10.5194/os-17-1815-2021 · doi-reference
Event-based wave statistics for the Baltic Sea
10.5194/sp-4-osr8-10-2024 · doi-reference
Using random forests to forecast daily extreme sea level occurrences at the Baltic Coast
10.5194/nhess-25-1139-2025 · doi-reference
An innovative deep learning-based approach for significant wave height forecasting
10.1016/j.oceaneng.2025.120623 · doi-reference
Ensemble robust local mean decomposition integrated with random forest for short-term significant wave height forecasting
10.1016/j.renene.2023.01.108 · doi-reference