Statistical analysis and prediction of force and overtopping rates on large scale vertical walls using support vector machine and random forest regression

This study provides a statistical basis to determine the most influencing parameters on forces and overtopping over vertical walls, as well as a framework for the application of machine learning models in coastal engineering. To this end, horizontal force and overtopping data for regular waves of varying height (0.63m-1.65m), period (4s-8s) and water depth (3.37m-3.97m) over a vertical wall has been studied using Redundancy Analysis (RDA) and regressed using Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Random Forest Regression (RFR). The RDA shows that about 60% of the output variables variance can be explained by the structure dimensions and 15% by the incoming wave characteristics. The SVR approach better predicted the average force (MRE=39.9% and R2=0.346), whereas the RFR technique better predicted overtopping discharges (MRE=46.7% and R2=0.802). By expanding the database, the error on overtopping prediction was reduced to 22.1% and 27.5% respectively for the SVR and RFR.