{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T19:39:18Z","timestamp":1774294758617,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ciencia e Tecnologia","award":["1801P.01023"],"award-info":[{"award-number":["1801P.01023"]}]},{"name":"Funda\u00e7\u00e3o para a Ciencia e Tecnologia","award":["UIDB\/UIDP\/00134\/2020"],"award-info":[{"award-number":["UIDB\/UIDP\/00134\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMSE"],"abstract":"<jats:p>The existence of multiple wave forecasts leads to the question of which one should be used in practical ocean engineering applications. Ensemble forecasts have emerged as an important complement to deterministic forecasts, with better performances at mid-to-long ranges; however, they add another option to the variety of wave predictions that are available nowadays. This study developed random forest (RF) postprocessing models to identify the best wave forecast between two National Centers for Environmental Protection (NCEP) products (deterministic and ensemble). The supervised learning classifier was trained using National Data Buoy Center (NDBC) buoy data and the RF model accuracies were analyzed as a function of the forecast time. A careful feature selection was performed by evaluating the impact of the wind and wave variables (inputs) on the RF accuracy. The results showed that the RF models were able to select the best forecast only in the very short range using input information regarding the significant wave height, wave direction and period, and ensemble spread. At forecast day 5 and beyond, the RF models could not determine the best wave forecast with high accuracy; the feature space presented no clear pattern to allow for successful classification. The challenges and limitations of such RF predictions for longer forecast ranges are discussed in order to support future studies in this area.<\/jats:p>","DOI":"10.3390\/jmse9030298","type":"journal-article","created":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T12:12:18Z","timestamp":1615205538000},"page":"298","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Operational Wave Forecast Selection in the Atlantic Ocean Using Random Forests"],"prefix":"10.3390","volume":"9","author":[{"given":"Ricardo M.","family":"Campos","sequence":"first","affiliation":[{"name":"Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal"}]},{"given":"Mariana O.","family":"Costa","sequence":"additional","affiliation":[{"name":"Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal"}]},{"given":"Fabio","family":"Almeida","sequence":"additional","affiliation":[{"name":"Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8570-4263","authenticated-orcid":false,"given":"C.","family":"Guedes Soares","sequence":"additional","affiliation":[{"name":"Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1080\/17445300903210988","article-title":"Robust Pareto-optimum routing of ships utilizing deterministic and ensemble weather forecasts","volume":"5","author":"Hinnenthal","year":"2010","journal-title":"Ships Offshore Struct."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.oceaneng.2016.06.035","article-title":"Development of a ship weather routing system","volume":"123","author":"Vettor","year":"2016","journal-title":"Ocean Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1016\/j.oceaneng.2016.09.007","article-title":"Weather Routing and Safe Ship Handling in the Future of Shipping","volume":"130","author":"Perera","year":"2017","journal-title":"Ocean Eng."},{"key":"ref_4","unstructured":"Fu, T., Babanin, A., Bentamy, A., Campos, R., Dong, S., Gramstad, O., Kapsenberg, G., Mao, W., Miyake, R., and Murphy, A.J. (2018, January 9\u201314). Committee No I.1: Environment. Proceedings of the 20th International Ship and Offshore Structures Congress, Liege, Belgium."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.oceaneng.2015.07.027","article-title":"Directional analysis of sea storms","volume":"107","author":"Laface","year":"2015","journal-title":"Ocean Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.ocemod.2014.07.005","article-title":"Extreme wave parameters under North Atlantic extratropical cyclones","volume":"81","year":"2014","journal-title":"Ocean Model."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.ocemod.2018.02.002","article-title":"Extreme wind-wave modeling and analysis in the south Atlantic ocean","volume":"124","author":"Campos","year":"2018","journal-title":"Ocean Model."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108111","DOI":"10.1016\/j.oceaneng.2020.108111","article-title":"Analysis of Atlantic extratropical storm tracks characteristics in 41 years of ERA5 and CFSR\/CFSv2 Databases","volume":"216","author":"Gramcianinov","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107745","DOI":"10.1016\/j.oceaneng.2020.107745","article-title":"Extreme waves generated by cyclonic winds in the western portion of the South Atlantic Ocean","volume":"213","author":"Gramcianinov","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.pocean.2007.05.005","article-title":"Wave modelling\u2014The state of the art","volume":"75","author":"Cavaleri","year":"2007","journal-title":"Prog. Oceanogr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1175\/WAF-D-18-0086.1","article-title":"Assessments of surface winds and waves from the NCEP Ensemble Forecast System","volume":"33","author":"Campos","year":"2018","journal-title":"Weather Forecast."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1111\/j.2153-3490.1965.tb01424.x","article-title":"A Study of the Predictability of a 28-Variable Atmospheric Model","volume":"17","author":"Lorenz","year":"1965","journal-title":"Tellus"},{"key":"ref_13","unstructured":"Lorenz, E.N. (1967). The Nature and Theory of the General Circulation of the Atmosphere, World Meteorological Organization."},{"key":"ref_14","unstructured":"Chen, H.S. (, January November). Ensemble prediction of ocean waves at NCEP. Proceedings of the 28th Ocean Engineering Conference, Kaohsiung, Taiwan."},{"key":"ref_15","unstructured":"Cao, D., Chen, H.S., and Tolman, H. (2007, January 11\u201316). Verification of ocean wave ensemble forecasts at NCEP. Proceedings of the 10th International Workshop on Wave Hindcasting and Forecasting and First Coastal Hazards Symposium, Camp Springs, MD, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1007\/s10236-019-01329-4","article-title":"Global assessments of the NCEP Ensemble Forecast System using altimeter data","volume":"70","author":"Campos","year":"2020","journal-title":"Ocean Dyn."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1175\/BAMS-D-12-00032.1","article-title":"The NCEP\u2013FNMOC combined wave ensemble product. Expanding benefits of interagency probabilistic forecasts to the oceanic environment","volume":"94","author":"Alves","year":"2013","journal-title":"Bull. Am. Meteorol. Soc. BAMS"},{"key":"ref_18","first-page":"31","article-title":"Twenty-one years of wave forecast verification","volume":"150","author":"Bidlot","year":"2017","journal-title":"ECMWF Newsl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1016\/S0029-8018(00)00027-5","article-title":"Neural networks for wave forecasting","volume":"28","author":"Deo","year":"2001","journal-title":"Ocean Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.cageo.2004.10.005","article-title":"Artificial neural networks in wave predictions at the west coast of Portugal","volume":"31","author":"Makarynskyy","year":"2005","journal-title":"Comput. Geosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1533\/saos.2004.0005","article-title":"Neural networks in ocean engineering","volume":"1","author":"Jain","year":"2006","journal-title":"Ships Offshore Struct."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/j.oceaneng.2016.10.033","article-title":"Regional ocean wave height prediction using sequential learning neural networks","volume":"129","author":"Kumar","year":"2017","journal-title":"Ocean Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101617","DOI":"10.1016\/j.ocemod.2020.101617","article-title":"Improving NCEP\u2019s global-scale wave ensemble averages using neural networks","volume":"149","author":"Campos","year":"2020","journal-title":"Ocean Model."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.oceaneng.2012.01.017","article-title":"Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time","volume":"43","author":"Deka","year":"2012","journal-title":"Ocean Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.apor.2016.04.011","article-title":"Prediction of extreme wave heights using neuro wavelet technique","volume":"58","author":"Dixit","year":"2016","journal-title":"Appl. Ocean Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.oceaneng.2017.12.044","article-title":"Real-time forecasting of wave heights using EOF-wavelet-neural network hybrid model","volume":"150","author":"Oh","year":"2018","journal-title":"Ocean Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.oceano.2017.03.007","article-title":"Application of neural networks and support vector machine for significant wave height prediction","volume":"59","author":"Ocvirk","year":"2017","journal-title":"Oceanologia"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.apor.2008.11.001","article-title":"An alternative approach for the prediction of significant wave heights based on classification and regression trees","volume":"30","author":"Mahjoobi","year":"2008","journal-title":"Appl. Ocean Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"102339","DOI":"10.1016\/j.apor.2020.102339","article-title":"Using random forest and gradient boosting trees to improve wave forecast at a specific location","volume":"104","author":"Callens","year":"2020","journal-title":"Appl. Ocean Res."},{"key":"ref_30","first-page":"26","article-title":"Comparison and assessment of three wave hindcasts in the North Atlantic Ocean","volume":"9","author":"Campos","year":"2016","journal-title":"J. Oper. Oceanogr."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.ocemod.2013.12.006","article-title":"Intercomparison of wind and wave data from the ECMWF Reanalysis Interim and the NCEP Climate Forecast System Reanalysis","volume":"75","author":"Stopa","year":"2014","journal-title":"Ocean Model."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1989","DOI":"10.1175\/WAF-D-17-0023.1","article-title":"Performance of the new NCEP global ensemble forecast system in a parallel experiment","volume":"32","author":"Zhou","year":"2017","journal-title":"Weather Forecast."},{"key":"ref_33","unstructured":"Tolman, H., Accensi, M., Alves, J.H., Ardhuin, F., Bidlot, J., Booij, N., Bennis, A.C., Campbell, T., Chalikov, D., and Chawla, A. (2019). User manual and System Documentation of WAVEWATCH III R Version."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1175\/2010JPO4324.1","article-title":"Semiempirical dissipation source functions for ocean waves. Part I: Definition, calibration, and validation","volume":"40","author":"Ardhuin","year":"2010","journal-title":"J. Phys. Oceanogr."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.jmarsys.2008.05.014","article-title":"Summary diagrams for coupled hydrodynamic-ecosystem model skill assessment","volume":"76","author":"Jolliff","year":"2009","journal-title":"J. Mar. Syst."},{"key":"ref_36","unstructured":"Khaire, U.M., Dhanalakshmi, R., and Stability of feature selection algorithm: A review (2019). J. King Saud Univ. Comput. Inf. Sci., Available online: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1319157819304379."},{"key":"ref_37","unstructured":"Breiman, L., Friedman, J., Stone, C.J., and Olshen, R.A. (1984). Classification and Regression Trees, CRC Press."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Strobl, C., Boulesteix, A.-L., Zeileis, A., and Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinform., 8.","DOI":"10.1186\/1471-2105-8-25"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene selection for cancer classification using support vector machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_41","unstructured":"Parr, T., Turgutlu, K., Csiszar, C., and Howard, J. (2020, November 01). Beware Default Random Forest Importances. Available online: https:\/\/explained.ai\/rf-importance\/."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/s11222-016-9646-1","article-title":"Correlation and variable importance in random forests","volume":"27","author":"Gregorutti","year":"2017","journal-title":"Stat. Comput."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zaki, M.J., and Meira, W. (2020). Data Mining and Machine Learning: Fundamental Concepts and Algorithms, Cambridge University Press.","DOI":"10.1017\/9781108564175"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Friedman, J., Hastie, T., and Tibshirani, R. (2009). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1145\/507338.507355","article-title":"Data mining: Practical machine learning tools and techniques with java implementations","volume":"31","author":"Witten","year":"2002","journal-title":"ACM Sigmod. Record"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1002\/widm.1301","article-title":"Hyperparameters and tuning strategies for random forest","volume":"9","author":"Probst","year":"2019","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_47","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Oshiro, T.M., Perez, P.S., and Baranauskas, J.A. (2012). How many Trees in a Random Forest? International Workshop on Machine Learning and Data Mining in Pattern Recognition, Springer.","DOI":"10.1007\/978-3-642-31537-4_13"}],"container-title":["Journal of Marine Science and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-1312\/9\/3\/298\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:34:51Z","timestamp":1760160891000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-1312\/9\/3\/298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,8]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["jmse9030298"],"URL":"https:\/\/doi.org\/10.3390\/jmse9030298","relation":{},"ISSN":["2077-1312"],"issn-type":[{"value":"2077-1312","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,8]]}}}