[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117455-en":3,"doc-seo-117455-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117455,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Enhancing downscaled ocean wave conditions with machine learning and wave spectra","Machine Learning (ML) is applied to downscale offshore ocean wave conditions to a nearshore location using detailed 1D wave spectra representations of the offshore wave field. The study examines sensitivities in input data and compares different approaches for the downscaling task, aiming to improve predictive performance. Results show that incorporating 1D wave spectra enhances ML downscaling, achieving a 27% reduction in root mean squared error for significant wave height versus an integrated-parameter-only ML baseline. Long-Term Short-Term Memory improves performance overall, but no universal model works for all wave parameters.","Enhancing downscaled ocean wave conditions with machine learning and wave spectra  \nAuthor  \nPeach , Leo , Cartwright , Nick , Viera da Silva , Guilherme , Strauss , Darrell  \nPublished 2025  \nJournal Title Ocean Modelling  \nVersion  \nVersion of Record (VoR)  \nDOI  \n10.1016/j.ocemod.2025.102502  \nRights statement  \n© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)) .  \nDownloaded from  \n[https://hdl.handle.net/10072/437125](https://hdl.handle.net/10072/437125)  \nGriffith Research Online  \n[https://research-repository.griffith.edu.au](https://research-repository.griffith.edu.au)  \nOcean Modelling 194 (2025) 102502  \nContents lists available at ScienceDirect  \nOcean Modelling  \njournal [homepage: www.elsevier.com/locate/ocemod](homepage: www.elsevier.com/locate/ocemod)  \n| Enhancing downscaled ocean wave conditions with machine learning and   wave spectra\u003Cbr>Leo Peach a,b,* , Nick Cartwright a,b, Guilherme Viera da Silva b, Darrell Strauss b\u003Cbr>a School of Engineering and the Built Environment & Coastal and Marine Research Centre, Griffith University, Australia b Coastal and Marine Research Centre, Cities Research Institute, Griffith University, Australia |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords: Machine learning Neural network Wave spectra |  | Machine Learning (ML) is becoming an increasingly popular and important tool for predicting ocean wave conditions. Here it is applied to downscale offshore conditions to a nearshore location utilising more detailed representations of the offshore wave field using 1D wave spectra. Our aim is to identify some of the sensitivities in input data when using machine learning to conduct downscaling (a common application) and present results from different approaches. The results demonstrate that downscaling wave conditions using ML can be enhanced using 1D wave spectra to improve performance. Here, we obtained a 27 % reduction in root mean squared error in significant wave height when compared to integrated parameter only machine learning approach with performance improved when using 1D wave spectra. Though we identified that the Long-Term Short-Term Memory approach applied here improved performance overall, it also appears there is not a one-size fits-all approach for all wave parameters. Careful feature selection (which features to include or exclude when training a model), feature engineering (such as feature encoding and sequence selection) and model configuration continue to be key factors in achieving accurate wave conditions. |\n\n1. Introduction  \nThe downscaling of wave heights is important for a range of maritime and coastal applications, from port operations and vessel movements to disaster management, coastal engineering, and recreation. Whether the application be hindcasting or forecasting ocean conditions, one of the challenges of accurately obtaining wave conditions at the coast is the ability to downscale predictions from oceanographic wave models to the coast quantifying additional processes that occur as waves approach the coast, such as depth induced wave breaking, refraction and diffraction. Accurate downscaling is critical for shipping, in particular vessel movements into and out of ports, many make use of under keel clearance systems which rely on highly accurate predictions of wave parameters. Downscaling typically involves the development of computationally expensive physics-based models (such as a phaseaveraged spectral wave models), which also require good quality input data, including bathymetry, to perform well. A range of other techniques for downscaling have been proposed including the Hybrid Downscaling Technique (Peach et al., 2023; Vieira Da Silva et al., 2018), Backward Ray Tracing (Crosby et al., 2019; Oh, 1981), Look-up tables (EA, 2016), statistical models (Hegermiller et al","cbCaidnBUhwnLJYO","https://ap.wps.com/l/cbCaidnBUhwnLJYO","pdf",6250592,1,13,"English","en",105,"# Introduction\n## Problem and importance of wave downscaling\n## Prior techniques and challenges\n## Machine learning and deep learning approaches","[{\"question\":\"How does the paper apply machine learning to ocean wave downscaling?\",\"answer\":\"It uses machine learning to transform offshore wave conditions to a nearshore location, leveraging more detailed offshore information represented by 1D wave spectra.\"},{\"question\":\"What improvement is reported when using 1D wave spectra?\",\"answer\":\"Using 1D wave spectra improves performance, yielding a 27% reduction in root mean squared error for significant wave height compared with an integrated-parameter-only ML approach.\"},{\"question\":\"Do the authors find a single best model for all wave parameters?\",\"answer\":\"No. While Long-Term Short-Term Memory improves performance overall, the paper indicates there is no one-size-fits-all approach across different wave parameters.\"}]","Enhancing downscaled ocean wave conditions with machine learning and wave spectra | 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does the paper apply machine learning to ocean wave downscaling?","Question",{"text":75,"@type":76},"It uses machine learning to transform offshore wave conditions to a nearshore location, leveraging more detailed offshore information represented by 1D wave spectra.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What improvement is reported when using 1D wave spectra?",{"text":80,"@type":76},"Using 1D wave spectra improves performance, yielding a 27% reduction in root mean squared error for significant wave height compared with an integrated-parameter-only ML approach.",{"name":82,"@type":73,"acceptedAnswer":83},"Do the authors find a single best model for all wave parameters?",{"text":84,"@type":76},"No. While Long-Term Short-Term Memory improves performance overall, the paper indicates there is no one-size-fits-all approach across different wave 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