Preprints
https://doi.org/10.5194/nhess-2022-221
https://doi.org/10.5194/nhess-2022-221
 
05 Sep 2022
05 Sep 2022
Status: this preprint is currently under review for the journal NHESS.

A predictive equation for wave setup using genetic programming

Charline Dalinghaus1, Giovanni Coco1, and Pablo Higuera2,3 Charline Dalinghaus et al.
  • 1School of Environment, Faculty of Science, The University of Auckland, New Zealand
  • 2Department of Civil and Environmental Engineering, The University of Auckland, New Zealand
  • 3Department of Civil and Environmental Engineering, National University of Singapore, Singapore

Abstract. We applied machine learning to improve the accuracy of present predictors of wave setup. Namely, we used an evolutionary-based genetic programming model and a previously published dataset, which includes various beach and wave conditions. Here, we present two new wave setup predictors, a simple predictor, which is a function of wave height, wavelength, and beach slope, and a fitter, but more complex predictor, which is also a function of sediment diameter. The results show that the new predictors outperform existing formulas. Therefore, we conclude that machine learning models are capable of not only improving prediction capability (when compared to classical predictors) but also of providing physically sound descriptions of the processes modelled.

Charline Dalinghaus et al.

Status: open (until 20 Oct 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Charline Dalinghaus et al.

Data sets

Observations of Wave Runup, Setup, and Swash on Natural Beaches Hilary F. Stockdon and Rob A. Holman https://pubs.usgs.gov/ds/602/

Model code and software

GpLearn_WaveSetup Charline Dalinghaus https://github.com/chardalinghaus/GpLearn_WaveSetup

Charline Dalinghaus et al.

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Short summary
Wave setup is a critical component of coastal flooding. Consequently, understanding and being able to predict wave setup is vital to protect coastal resources and people living near the shore. Here, we applied machine learning to improve the accuracy of present predictors of wave setup. The results show that the new predictors outperform existing formulas demonstrating the capability of machine learning models to provide physically sound descriptions of the processes modelled.
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