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Updated: Jul 19, 2025

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Enhanced wave overtopping simulation at vertical breakwaters using machine learning algorithms.

M A Habib1, J J O'Sullivan1,2, S Abolfathi3

  • 1UCD Dooge Centre for Water Resources Research, School of Civil Engineering, University College Dublin, Dublin, Ireland.

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Summary
This summary is machine-generated.

Machine learning models accurately predict wave overtopping at vertical seawalls. Random Forest and Gradient Boosted Decision Trees offer superior performance and computational efficiency for coastal protection strategies.

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Area of Science:

  • Coastal engineering
  • Hydraulics
  • Machine learning applications

Background:

  • Accurate wave overtopping prediction is crucial for coastal defense against rising sea levels and storm surges.
  • Traditional methods rely on empirical relations from physical and numerical models.
  • Machine learning (ML) offers advanced data-driven approaches for overtopping prediction.

Purpose of the Study:

  • To evaluate the performance of four ML techniques for predicting wave overtopping discharge at vertical seawalls.
  • To compare the predictive accuracy and computational efficiency of Random Forest, Gradient Boosted Decision Trees, Support Vector Machines-Regression, and Artificial Neural Networks.
  • To investigate the suitability of ML for wave overtopping assessment in diverse coastal conditions.

Main Methods:

  • Development of ML models using the EurOtop (2018) database.
  • Application of hyperparameter tuning for algorithm optimization.
  • Utilizing feature transformation and selection to reduce data redundancy and prevent overfitting.
  • Statistical analysis to compare the performance of Random Forest, Gradient Boosted Decision Trees, Support Vector Machines-Regression, and Artificial Neural Networks.

Main Results:

  • Random Forest demonstrated superior predictive performance, followed by Gradient Boosted Decision Trees, Support Vector Machines-Regression, and Artificial Neural Networks.
  • Decision Tree-based methods (Random Forest, Gradient Boosted Decision Trees) were more computationally efficient than Support Vector Machines-Regression and Artificial Neural Networks.
  • Gradient Boosted Decision Trees exhibited the fastest simulation times.

Conclusions:

  • Machine learning approaches provide a reliable and computationally effective method for wave overtopping evaluation at vertical seawalls.
  • Random Forest and Gradient Boosted Decision Trees are recommended for their accuracy and efficiency in coastal engineering applications.
  • ML models can effectively assess wave overtopping across a broad spectrum of hydrodynamic and structural conditions.