Artificial neural networks for predicting maximum wave runup on rubble mound structures

T. Erdik*, M. E. Savci, Z. Şen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

Accurate prediction of maximum wave runup on breakwaters is a vital issue for determining crest level of coastal structures. In practice, traditional regression-based empirical model, recommended by the "Coastal Engineering Manual", as well as the "Manual on the use of rock in hydraulic engineering", is widely used. However, use of these approaches brings additional restrictive assumptions such as linearity, normality (Gaussian distributed variables), variance constancy (homoscedasticity) etc. This paper focuses on the prediction of maximum wave runup elevation through artificial neural networks (ANNs), which has no restrictive assumptions. Out of 261 irregular wave runup data of Van der Meer and Stam, 100 randomly chosen data points are used for training the model. The remaining data are exploited for testing purposes. This study has two objectives: (1) to develop ANN models and search their applicability to estimate maximum wave runup elevation on breakwaters; (2) to compare widely used empirical model with these models. For these purposes, different ANN models are constructed and trained with their own topology. The performance of the ANN models is tested against the same testing data, none of which is employed in the training. It is found that ANN technique gives more accurate results and the extent of accuracy can be affected by the structure of ANNs.

Original languageEnglish
Pages (from-to)6403-6408
Number of pages6
JournalExpert Systems with Applications
Volume36
Issue number3 PART 2
DOIs
Publication statusPublished - Apr 2009

Keywords

  • Artificial neural networks
  • Dimensionless 2% wave runup
  • Rock slopes

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