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

Abstract

This study provides a statistical basis to determine the most influencing parameters on forces and overtopping over vertical walls, as well as to showcase the usability of machine learning modelling in coastal engineering. To this end, horizontal force and overtopping data for regular waves of varying height (0.63–1.65 m), period (4–8 s), and water depth (3.37–3.97 m) over a vertical wall were studied using redundancy analysis (RDA) and regressed using multiple linear regression, support vector regression (SVR), and random forest regression (RFR). The RDA showed that about 60% of the output variable variance can be explained by the structure dimensions and 15% by the incoming wave characteristics. The SVR approach better predicted the average force (mean relative error (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.