Loading...
Please wait, while we are loading the content...
Similar Documents
Prediction of Mean Wave Overtopping Discharge Using Gradient Boosting Decision Trees
Content Provider | MDPI |
---|---|
Author | Bieman, Joost P. Den Wilms, Josefine M. Boogaard, Henk F. P. Van Den Gent, Marcel R. A. Van |
Copyright Year | 2020 |
Description | Wave overtopping is an important design criterion for coastal structures such as dikes, breakwaters and promenades. Hence, the prediction of the expected wave overtopping discharge is an important research topic. Existing prediction tools consist of empirical overtopping formulae, machine learning techniques like neural networks, and numerical models. In this paper, an innovative machine learning method—gradient boosting decision trees—is applied to the prediction of mean wave overtopping discharges. This new machine learning model is trained using the CLASH wave overtopping database. Optimizations to its performance are realized by using feature engineering and hyperparameter tuning. The model is shown to outperform an existing neural network model by reducing the error on the prediction of the CLASH database by a factor of 2.8. The model predictions follow physically realistic trends for variations of important features, and behave regularly in regions of the input parameter space with little or no data coverage. |
Starting Page | 1703 |
e-ISSN | 20734441 |
DOI | 10.3390/w12061703 |
Journal | Water |
Issue Number | 6 |
Volume Number | 12 |
Language | English |
Publisher | MDPI |
Publisher Date | 2020-06-15 |
Access Restriction | Open |
Subject Keyword | Water Marine Engineering Machine Learning Artificial Intelligence Wave Overtopping Physical Model Tests Gradient Boosting Neural Network |
Content Type | Text |
Resource Type | Article |