A predictive equation for wave setup through the use of genetic programming

As the sea level rises, coastal flooding is predicted to increase worldwide. One of the processes contributing to estimating coastal flooding is wave setup (combined with swash), which accounts for the effect of breaking waves on water levels. Defined as the superelevation of the mean water level due to breaking waves (Longuet-Higgins and Stewart, 1964), wave setup prediction has been a research topic of interest for decades. However, empirical predictors still show considerable scatter, making this component the one with the largest uncertainty when estimating flooding levels. At the same time, more data has become available, opening the possibility of using data-driven models, such as machine learning, a powerful tool that has already been applied to predict a variety of coastal processes. Our work aims to develop a new, robust and reliable wave setup predictor through the use of an evolutionary-based genetic programming technique. To develop the algorithm, we use a dataset compiled by Stockdon et al. (2006), containing 491 measurements from 10 field experiments representing different beach and wave conditions.