No Cover Image

Journal article 64 views 21 downloads

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

Check full text

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...

Full description

Published in: Applied Ocean Research
ISSN: 0141-1187
Published: Elsevier BV 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa65710
Tags: Add Tag
No Tags, Be the first to tag this record!
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 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.
Keywords: Wave attenuation, Coastal vegetation, XBeach, Machine learning
College: Faculty of Science and Engineering
Funders: KI's PhD is supported by the Engineering and Physical Sciences Research Council (EPSRC) UK Doctoral Training Partnership of Swansea University.

Similar Items