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Forecasting Nearshore Wave Conditions With Machine Learning Methods / MAHSA MOKHTARI

Swansea University Author: MAHSA MOKHTARI

  • E-Thesis under embargo until: 25th January 2027

DOI (Published version): 10.23889/SUThesis.70078

Abstract

Accurate wave height prediction is crucial for maritime safety, coastal management, and climate research. This thesis explores the application of advanced Machine Learning models including Linear Regression (LR), Convolutional Neural Networks (CN N ), Convolutional Neural Network-Long Short-Term Mem...

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Published: Swansea University, Wales, UK 2024
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Karunarathna, H. U., & Giannetti, C.
URI: https://cronfa.swan.ac.uk/Record/cronfa70078
first_indexed 2025-07-31T12:44:24Z
last_indexed 2025-08-01T14:34:00Z
id cronfa70078
recordtype RisThesis
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For the purpose of site-speci&#xFB01;c o&#xFB00;shore data extraction, di&#xFB00;erences in the complete-ness and quality of databases across seasons have been taken into account. A thorough coverage of di&#xFB00;erent nearshore sites-from Isles of Scilly (2015-2019) and West Gabbard (2019-2022) is shown to account for di&#xFB00;erent periods and conditions. A comprehensive search of 31 feature sets is conducted to optimise the model&#x2019;s performance.Feature selection is implemented out to obtain the relevant features, and parameter tuning is applied to the model parameters, providing two distinct layers of optimisation. The models are compared against each other in a comparison experiment that analysed the predictive accuracy of using the model against di&#xFB00;erent periods of data and for various regional wave dynamics.The results indicate that both deep learning and ensemble models can accurately predict wave heights in both locations despite data length and wave behaviour being di&#xFB00;erent across the studies. The XGBoost model has been very successful in predicting both spatial and temporal wave structures, while CN N and CN N &#x2212;LST M models still performed well, which emphasisestheir capabilities in portraying complex waves. Additionally, this study addresses challenges such as data dependency, over&#xFB01;tting and the computational demands of large-scale datasets. This thesis presents a framework that signi&#xFB01;cantly enhances wave height forecasting for both locations by integrating features such as wave height (Hs), wave direction (Dir), wave period (T m), wave spread (Spr) and wave speed (Cv). The results highlight the adaptability and strength of the models, showing that both deep learning and ensemble methodologies can reach high levels of predictive power with careful optimisation of parameters, as well as detailed datasets. 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spelling 2025-07-31T13:48:01.3923146 v2 70078 2025-07-31 Forecasting Nearshore Wave Conditions With Machine Learning Methods b74649af92a12d12e4529f3f2892b642 MAHSA MOKHTARI MAHSA MOKHTARI true false 2025-07-31 Accurate wave height prediction is crucial for maritime safety, coastal management, and climate research. This thesis explores the application of advanced Machine Learning models including Linear Regression (LR), Convolutional Neural Networks (CN N ), Convolutional Neural Network-Long Short-Term Memory (CN N -LST M ), Random Forest Regressor (RF R) and Extreme Gradient Boosting (XGBoost) to predict nearshore wave heights for Looe Bay and Felixstowe. For the purpose of site-specific offshore data extraction, differences in the complete-ness and quality of databases across seasons have been taken into account. A thorough coverage of different nearshore sites-from Isles of Scilly (2015-2019) and West Gabbard (2019-2022) is shown to account for different periods and conditions. A comprehensive search of 31 feature sets is conducted to optimise the model’s performance.Feature selection is implemented out to obtain the relevant features, and parameter tuning is applied to the model parameters, providing two distinct layers of optimisation. The models are compared against each other in a comparison experiment that analysed the predictive accuracy of using the model against different periods of data and for various regional wave dynamics.The results indicate that both deep learning and ensemble models can accurately predict wave heights in both locations despite data length and wave behaviour being different across the studies. The XGBoost model has been very successful in predicting both spatial and temporal wave structures, while CN N and CN N −LST M models still performed well, which emphasisestheir capabilities in portraying complex waves. Additionally, this study addresses challenges such as data dependency, overfitting and the computational demands of large-scale datasets. This thesis presents a framework that significantly enhances wave height forecasting for both locations by integrating features such as wave height (Hs), wave direction (Dir), wave period (T m), wave spread (Spr) and wave speed (Cv). The results highlight the adaptability and strength of the models, showing that both deep learning and ensemble methodologies can reach high levels of predictive power with careful optimisation of parameters, as well as detailed datasets. Furthermore, this study extends the use of Machine Learning in the area of oceanography, making a significant step towards more advanced systems of wave prediction in the future, with potential implications for coastal management focused on resilience. E-Thesis Swansea University, Wales, UK Nearshore wave height, Machine Learning, XGBoost, coastal engineering, time-series forecasting, UK coast 15 12 2024 2024-12-15 10.23889/SUThesis.70078 A selection of content is redacted or is partially redacted from this thesis to protect sensitive and personal information. COLLEGE NANME COLLEGE CODE Swansea University Karunarathna, H. U., & Giannetti, C. Doctoral Ph.D 2025-07-31T13:48:01.3923146 2025-07-31T13:27:02.2614047 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering MAHSA MOKHTARI 1 Under embargo Under embargo 2025-07-31T13:40:38.5405901 Output 67143811 application/pdf E-Thesis true 2027-01-25T00:00:00.0000000 Copyright: The author, Mahsa Mokhtari, 2025 Distributed under the terms of a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title Forecasting Nearshore Wave Conditions With Machine Learning Methods
spellingShingle Forecasting Nearshore Wave Conditions With Machine Learning Methods
MAHSA MOKHTARI
title_short Forecasting Nearshore Wave Conditions With Machine Learning Methods
title_full Forecasting Nearshore Wave Conditions With Machine Learning Methods
title_fullStr Forecasting Nearshore Wave Conditions With Machine Learning Methods
title_full_unstemmed Forecasting Nearshore Wave Conditions With Machine Learning Methods
title_sort Forecasting Nearshore Wave Conditions With Machine Learning Methods
author_id_str_mv b74649af92a12d12e4529f3f2892b642
author_id_fullname_str_mv b74649af92a12d12e4529f3f2892b642_***_MAHSA MOKHTARI
author MAHSA MOKHTARI
author2 MAHSA MOKHTARI
format E-Thesis
publishDate 2024
institution Swansea University
doi_str_mv 10.23889/SUThesis.70078
college_str Faculty of Science and Engineering
hierarchytype
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
document_store_str 0
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description Accurate wave height prediction is crucial for maritime safety, coastal management, and climate research. This thesis explores the application of advanced Machine Learning models including Linear Regression (LR), Convolutional Neural Networks (CN N ), Convolutional Neural Network-Long Short-Term Memory (CN N -LST M ), Random Forest Regressor (RF R) and Extreme Gradient Boosting (XGBoost) to predict nearshore wave heights for Looe Bay and Felixstowe. For the purpose of site-specific offshore data extraction, differences in the complete-ness and quality of databases across seasons have been taken into account. A thorough coverage of different nearshore sites-from Isles of Scilly (2015-2019) and West Gabbard (2019-2022) is shown to account for different periods and conditions. A comprehensive search of 31 feature sets is conducted to optimise the model’s performance.Feature selection is implemented out to obtain the relevant features, and parameter tuning is applied to the model parameters, providing two distinct layers of optimisation. The models are compared against each other in a comparison experiment that analysed the predictive accuracy of using the model against different periods of data and for various regional wave dynamics.The results indicate that both deep learning and ensemble models can accurately predict wave heights in both locations despite data length and wave behaviour being different across the studies. The XGBoost model has been very successful in predicting both spatial and temporal wave structures, while CN N and CN N −LST M models still performed well, which emphasisestheir capabilities in portraying complex waves. Additionally, this study addresses challenges such as data dependency, overfitting and the computational demands of large-scale datasets. This thesis presents a framework that significantly enhances wave height forecasting for both locations by integrating features such as wave height (Hs), wave direction (Dir), wave period (T m), wave spread (Spr) and wave speed (Cv). The results highlight the adaptability and strength of the models, showing that both deep learning and ensemble methodologies can reach high levels of predictive power with careful optimisation of parameters, as well as detailed datasets. Furthermore, this study extends the use of Machine Learning in the area of oceanography, making a significant step towards more advanced systems of wave prediction in the future, with potential implications for coastal management focused on resilience.
published_date 2024-12-15T05:26:25Z
_version_ 1851550738710265856
score 11.090091

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