Wave overtopping is an important design criterion for coastal structures such as dikes, breakwaters and promenades. Hence, the prediction of the expected wave overtopping discharge is an important research topic. Existing prediction tools consist of empirical overtopping formulae, machine learning techniques like neural networks, and numerical models. In this paper, an innovative machine learning method—gradient boosting decision trees—is applied to the prediction of mean wave overtopping discharges. This new machine learning model is trained using the CLASH wave overtopping database. Optimizations to its performance are realized by using feature engineering and hyperparameter tuning. The model is shown to outperform an existing neural network model by reducing the error on the prediction of the CLASH database by a factor of 2.8. The model predictions follow physically realistic trends for variations of important features, and behave regularly in regions of the input parameter space with little or no data coverage.

This paper reviews the dynamics of infragravity (long-period) waves over reef systems and the consequences of these waves for operations in ports located behind reefs with particular attention to Western Australia. Swells which originate in the Southern Ocean generate long (infragravity) waves, which propagate to the coast. On the reef edge, the swell waves are largely dissipated, transferring energy to turbulence and heat but also in that process generating long wave energy. The remaining swell waves are dominated by the infragravity waves and propagate towards the mainland and into port basins where they cause moored ship motions with consequences for the operational downtime of the port’s operations. When contemplating solutions to mitigate the impact of the long wave problems, these may be addressed from two sides: from the load side (waves) and the strength side (mooring). The former will be discussed in this paper.