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.
Subject
General Earth and Planetary Sciences
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