The present work employs the so-called Evolutionary Polynomial Regression (EPR) algorithm to build up a formula for the assessment of mean wave overtopping discharge for smooth sea dikes and vertical walls. EPR is a data-mining tool that combines and integrates numerical regression and genetic programming. This technique is here employed to dig into the relationship between the mean discharge and main hydraulic and structural parameters that characterize the problem under study. The parameters are chosen based on the existing and most used semi-empirical formulas for wave overtopping assessment. Besides the structural freeboard or local wave height, the unified models highlight the importance of local water depth and wave period in combination with foreshore slope and dike slope on the overtopping phenomena, which are combined in a unique parameter that is defined either as equivalent or imaginary slope. The obtained models aim to represent a trade-off between accuracy and parsimony. The final formula is simple but can be employed for a preliminary assessment of overtopping rates, covering the full range of dike slopes, from mild to vertical walls, and of water depths from the shoreline to deep water, including structures with emergent toes.

中文翻译：

---

使用进化多项式范式确定平均波超顶的半经验模型

目前的工作采用了所谓的进化多项式回归（EPR）算法来建立一个公式，用于评估光滑海堤和垂直墙的平均波超顶流量。EPR是一种数据挖掘工具，将数字回归和遗传编程结合在一起。在这里，使用该技术来挖掘平均流量与表征所研究问题的主要水力和结构参数之间的关系。根据现有和最常用的半经验公式来选择参数，以进行波浪超顶评估。除了结构干舷或局部波浪高度外，统一模型还强调了局部水深和波浪周期与前滨坡度和堤防坡度相结合对过顶现象的重要性，它们结合在定义为等效斜率或虚斜率的唯一参数中。所获得的模型旨在代表准确性与简约性之间的权衡。最终公式很简单，但可用于覆盖率的初步评估，覆盖率范围涵盖从轻度墙到垂直墙的整个堤防坡度，以及从海岸线到深水的水深，包括带有露趾的结构。