Estimation οf Wave Overtopping Discharges at Coastal Structures with Combined Slopes using Machine Learning Techniques

Abstract

Coastal defense structures are of paramount importance in protecting coastal communities from the adverse impacts of severe weather events and flooding. This study uses machine learning techniques, specifically Decision Tree (DT), Gradient Boosted Tree (GBT), and Support Vector Machine (SVM) models, to estimate wave overtopping discharge at coastal structures with combined slopes employing the recently built EurOtop database. The models were evaluated by deploying statistical metrics and Taylor diagram visualization. The GBT model demonstrated a high level of accuracy in predicting wave-overtopping discharge. Compared to the other models, the scatter index of GBT (0.392) was lower than that of DT (0.512) and SVM (0.823). In terms of the R-index, GBT (0.991) was superior to DT (0.977) and SVM (0.943). The GBT results were also compared with those of previous works. The findings showed that the GBT model significantly decreased the overall error and provided accurate estimations of the wave-overtopping discharge.

Keywords

• coastal defense
• wave overtopping
• prediction
• gradient boosted trees
• decision trees
• support vector machines