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Gaussian process regression approach for predicting wave attenuation through rigid vegetation

KRISTIAN IONS, Alma Rahat Orcid Logo, Dominic Reeve Orcid Logo, Harshinie Karunarathna Orcid Logo

Applied Ocean Research, Volume: 145

Swansea University Authors: KRISTIAN IONS, Alma Rahat Orcid Logo, Dominic Reeve Orcid Logo, Harshinie Karunarathna Orcid Logo

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DOI (Published version): 10.1016/j.apor.2024.103935

Abstract

Numerical modelling in the coastal environment often requires highly skilled users and can be hindered by high computation costs and time requirements. Machine Learning (ML) techniques have the potential to overcome these limitations and complement existing methods. This is an exploratory investigat...

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Published in: Applied Ocean Research
ISSN: 0141-1187
Published: Elsevier BV 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa65710
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spelling v2 65710 2024-02-27 Gaussian process regression approach for predicting wave attenuation through rigid vegetation 0eaeea3a999ce1ef38ade9b3b5f26a22 KRISTIAN IONS KRISTIAN IONS true false 6206f027aca1e3a5ff6b8cd224248bc2 0000-0002-5023-1371 Alma Rahat Alma Rahat true false 3e76fcc2bb3cde4ddee2c8edfd2f0082 0000-0003-1293-4743 Dominic Reeve Dominic Reeve true false 0d3d327a240d49b53c78e02b7c00e625 0000-0002-9087-3811 Harshinie Karunarathna Harshinie Karunarathna true false 2024-02-27 Numerical modelling in the coastal environment often requires highly skilled users and can be hindered by high computation costs and time requirements. Machine Learning (ML) techniques have the potential to overcome these limitations and complement existing methods. This is an exploratory investigation utilising a Gaussian Process (GP) data-driven modelling approach that can reproduce, for the given range of conditions in this study, the results of a widely used process-based model, XBeachX, when applied to the challenging problem of wave attenuation through vegetation. This study utilises efficient sampling strategies for data exploration, providing a valuable framework for future studies. The GP model was trained on a synthetic dataset generated using the numerical model XBeachX, which was calibrated using laboratory measurements. Our findings indicate that well-trained ML models can strongly complement traditional modelling approaches, especially in an environment where data sources are increasingly available. We have also explored the underlying interactions of the GP model's input features and their relationship to the model's output through a sensitivity analysis. Journal Article Applied Ocean Research 145 Elsevier BV 0141-1187 Wave attenuation, Coastal vegetation, XBeach, Machine learning 1 4 2024 2024-04-01 10.1016/j.apor.2024.103935 COLLEGE NANME COLLEGE CODE Swansea University SU Library paid the OA fee (TA Institutional Deal) KI's PhD is supported by the Engineering and Physical Sciences Research Council (EPSRC) UK Doctoral Training Partnership of Swansea University. 2024-03-25T13:19:12.7609816 2024-02-27T17:33:44.4508496 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering KRISTIAN IONS 1 Alma Rahat 0000-0002-5023-1371 2 Dominic Reeve 0000-0003-1293-4743 3 Harshinie Karunarathna 0000-0002-9087-3811 4 65710__29598__840107ed768a4260b9ee04ab7fb68661.pdf 65710_VoR.pdf 2024-02-28T15:47:36.6829216 Output 8990513 application/pdf Version of Record true Released under a Creative Commons license (CC-BY). true eng https://doi.org/10.1016/j.apor.2024.103935
title Gaussian process regression approach for predicting wave attenuation through rigid vegetation
spellingShingle Gaussian process regression approach for predicting wave attenuation through rigid vegetation
KRISTIAN IONS
Alma Rahat
Dominic Reeve
Harshinie Karunarathna
title_short Gaussian process regression approach for predicting wave attenuation through rigid vegetation
title_full Gaussian process regression approach for predicting wave attenuation through rigid vegetation
title_fullStr Gaussian process regression approach for predicting wave attenuation through rigid vegetation
title_full_unstemmed Gaussian process regression approach for predicting wave attenuation through rigid vegetation
title_sort Gaussian process regression approach for predicting wave attenuation through rigid vegetation
author_id_str_mv 0eaeea3a999ce1ef38ade9b3b5f26a22
6206f027aca1e3a5ff6b8cd224248bc2
3e76fcc2bb3cde4ddee2c8edfd2f0082
0d3d327a240d49b53c78e02b7c00e625
author_id_fullname_str_mv 0eaeea3a999ce1ef38ade9b3b5f26a22_***_KRISTIAN IONS
6206f027aca1e3a5ff6b8cd224248bc2_***_Alma Rahat
3e76fcc2bb3cde4ddee2c8edfd2f0082_***_Dominic Reeve
0d3d327a240d49b53c78e02b7c00e625_***_Harshinie Karunarathna
author KRISTIAN IONS
Alma Rahat
Dominic Reeve
Harshinie Karunarathna
author2 KRISTIAN IONS
Alma Rahat
Dominic Reeve
Harshinie Karunarathna
format Journal article
container_title Applied Ocean Research
container_volume 145
publishDate 2024
institution Swansea University
issn 0141-1187
doi_str_mv 10.1016/j.apor.2024.103935
publisher Elsevier BV
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
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description Numerical modelling in the coastal environment often requires highly skilled users and can be hindered by high computation costs and time requirements. Machine Learning (ML) techniques have the potential to overcome these limitations and complement existing methods. This is an exploratory investigation utilising a Gaussian Process (GP) data-driven modelling approach that can reproduce, for the given range of conditions in this study, the results of a widely used process-based model, XBeachX, when applied to the challenging problem of wave attenuation through vegetation. This study utilises efficient sampling strategies for data exploration, providing a valuable framework for future studies. The GP model was trained on a synthetic dataset generated using the numerical model XBeachX, which was calibrated using laboratory measurements. Our findings indicate that well-trained ML models can strongly complement traditional modelling approaches, especially in an environment where data sources are increasingly available. We have also explored the underlying interactions of the GP model's input features and their relationship to the model's output through a sensitivity analysis.
published_date 2024-04-01T13:19:09Z
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