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Enhanced wave overtopping simulation at vertical breakwaters using machine learning algorithms
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Habib, M. A., O’Sullivan, J. J., Abolfathi, Soroush and Salauddin, M. (2023) Enhanced wave overtopping simulation at vertical breakwaters using machine learning algorithms. PLOS ONE, 18 (8). e0289318. doi:10.1371/journal.pone.0289318 ISSN 1932-6203.
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Official URL: https://doi.org/10.1371/journal.pone.0289318
Abstract
Accurate prediction of wave overtopping at sea defences remains central to the protection of lives, livelihoods, and infrastructural assets in coastal zones. In addressing the increased risks of rising sea levels and more frequent storm surges, robust assessment and prediction methods for overtopping prediction are increasingly important. Methods for predicting overtopping have typically relied on empirical relations based on physical modelling and numerical simulation data. In recent years, with advances in computational efficiency, data-driven techniques including advanced Machine Learning (ML) methods have become more readily applicable. However, the methodological appropriateness and performance evaluation of ML techniques for predicting wave overtopping at vertical seawalls has not been extensively studied. This study examines the predictive performance of four ML techniques, namely Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machines—Regression (SVR), and Artificial Neural Network (ANN) for overtopping discharge at vertical seawalls. The ML models are developed using data from the EurOtop (2018) database. Hyperparameter tuning is performed to curtail algorithms to the intrinsic features of the dataset. Feature Transformation and advanced Feature Selection methods are adopted to reduce data redundancy and overfitting. Comprehensive statistical analysis shows superior performance of the RF method, followed in turn by the GBDT, SVR, and ANN models, respectively. In addition to this, Decision Tree (DT) based methods such as GBDT and RF are shown to be more computationally efficient than SVR and ANN, with GBDT performing simulations more rapidly that other methods. This study shows that ML approaches can be adopted as a reliable and computationally effective method for evaluating wave overtopping at vertical seawalls across a wide range of hydrodynamic and structural conditions.
| Item Type: | Journal Article | ||||||
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| Subjects: | G Geography. Anthropology. Recreation > GC Oceanography T Technology > TC Hydraulic engineering. Ocean engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| SWORD Depositor: | Library Publications Router | ||||||
| Library of Congress Subject Headings (LCSH): | Ocean waves -- Simulation methods, Breakwaters , Shore protection, Sea level -- Observations -- Mathematical models, Sea-walls -- Design and construction | ||||||
| Journal or Publication Title: | PLOS ONE | ||||||
| Publisher: | Public Library of Science | ||||||
| ISSN: | 1932-6203 | ||||||
| Official Date: | 16 August 2023 | ||||||
| Dates: |
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| Volume: | 18 | ||||||
| Number: | 8 | ||||||
| Article Number: | e0289318 | ||||||
| DOI: | 10.1371/journal.pone.0289318 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 17 August 2023 | ||||||
| Date of first compliant Open Access: | 18 August 2023 | ||||||
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| Contributors: |
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