Wave overtopping predictions using an advanced machine learning technique

Joost P. den Bieman, Marcel R.A. van Gent, Henk F.P. van den Boogaard

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Coastal structures are often designed to a maximum allowable wave overtopping discharge, hence accurate prediction of the amount of wave overtopping is an important issue. Both empirical formulae and neural networks are among the commonly used prediction tools. In this work, a new model for the prediction of mean wave overtopping discharge is presented using the innovative machine learning technique XGBoost. The selection of features to train the model on is carefully substantiated, including the redefinition of existing features to obtain a better model performance. Confidence intervals are derived by tuning hyperparameters and applying bootstrap resampling. The quality of the model is tested against four new physical model data sets, and a thorough quantitative comparison with existing machine learning methods and empirical overtopping formulae is presented. The XGBoost model generally outperforms other methods for the test data sets with normally incident waves. All data-driven methods show less accuracy on oblique wave data, presumably because these conditions are underrepresented in the training data. The performance of the XGBoost model is significantly improved by adding a randomly selected part of the new oblique wave cases to the training data. In the end, this new model is shown to reduce errors on all data used in this work with a factor of up to 5 compared to existing overtopping prediction methods.

Original languageEnglish
Article number103830
Pages (from-to)1-12
Number of pages12
JournalCoastal Engineering
Volume166
DOIs
Publication statusPublished - 2021

Keywords

  • Coastal structures
  • Gradient boosting decision trees
  • Machine learning
  • Physical model tests
  • Wave overtopping
  • XGBoost

Fingerprint Dive into the research topics of 'Wave overtopping predictions using an advanced machine learning technique'. Together they form a unique fingerprint.

Cite this