A predictive equation for wave setup using genetic programming
-
Published:2023-06-16
Issue:6
Volume:23
Page:2157-2169
-
ISSN:1684-9981
-
Container-title:Natural Hazards and Earth System Sciences
-
language:en
-
Short-container-title:Nat. Hazards Earth Syst. Sci.
Author:
Dalinghaus CharlineORCID, Coco GiovanniORCID, Higuera PabloORCID
Abstract
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 foreshore 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. We conclude that machine learning models are capable of improving predictive capability (when compared to existing predictors) and also of providing a physically sound description of wave setup.
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
Reference50 articles.
1. Battjes, J. A.: Computation of set-up, longshore currents, run-up and
overtopping due to wind-generated waves, Ph.D. thesis, Delft University of
Technology, http://resolver.tudelft.nl/uuid:e126e043-a858-4e58-b4c7-8a7bc5be1a44 (last access: 5 April 2022),
1974. a, b, c 2. Beuzen, T. and Splinter, K.: Machine learning and coastal processes, in: Sandy
Beach Morphodynamics, edited by: Jackson, D. W. T. and Short, A. D.,
689–710, Elsevier, https://doi.org/10.1016/B978-0-08-102927-5.00028-X, 2020. a, b 3. Bowen, A., Inman, D., and Simmons, V.: Wave “set-down”and set-up, J. Geophys. Res., 73, 2569–2577,
https://doi.org/10.1029/JB073i008p02569, 1968. a, b, c, d, e 4. Camus, P., Mendez, F. J., and Medina, R.: A hybrid efficient method to
downscale wave climate to coastal areas, Coast. Eng., 58, 851–862,
https://doi.org/10.1016/j.coastaleng.2011.05.007, 2011. a, b 5. Coast and Ocean Collective: Data, Coast and Ocean Collective [data set], https://coastalhub.science/data (last access: 8 November 2022), 2019. a
|
|