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

A predictive equation for wave setup using genetic programming

Charline Dalinghaus, Giovanni Coco, and Pablo Higuera

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: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-221', Anonymous Referee #1, 10 Oct 2022
    • AC1: 'Reply on RC1', Charline Dalinghaus, 08 Nov 2022
  • RC2: 'Comment on nhess-2022-221', Francesca Ribas, 07 Dec 2022
    • AC2: 'Reply on RC2', Charline Dalinghaus, 23 Jan 2023

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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