Predicting wave reflection coefficient of vertical caisson breakwater using machine learning: A data-driven approach
Abstract
Coastal zones are vital for ecological balance and human development, but are increasingly threatened by wave activity, shoreline erosion, and sea-level rise. To mitigate these challenges, engineers employ coastal protection structures. Specifically, vertical caisson breakwaters are preferred in deeper waters due to their advantages. Reflection Coefficient is an important hydrodynamic performance indicator for breakwaters. Recently, machine learning (ML) has been used for predicting coastal engineering parameters, offering an efficient means to support or augment traditional physical model studies, particularly during preliminary design phases, if sufficient quality data is available. This research focuses on using ML models to estimate the reflection coefficient of vertical caisson breakwaters based on a limited set of experimental data. Four different algorithms-Artificial Neural Network (ANN), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB)-are developed and evaluated. Hyperparameters are optimised using a hybrid approach, combining Grid Search with manual refinement. Of the four models, XGB achieved the highest prediction accuracy (Test CC = 0.9631), while Random Forest exhibited the smallest generalisation gap, indicating strong consistency across datasets. The findings from the study suggest that XGB offers an efficient tool for early-stage design applications in coastal engineering.