  The accurate prediction of wave height attenuation due to vegetation is
crucial for designing effective and efficient natural and nature-based
solutions for flood mitigation, shoreline protection, and coastal ecosystem
preservation. Central to these predictions is the estimation of the vegetation
drag coefficient. The present study undertakes a comprehensive evaluation of
three distinct methodologies for estimating the drag coefficient: traditional
manual calibration, calibration using a novel application of state-of-the-art
metaheuristic optimization algorithms, and the integration of an established
empirical bulk drag coefficient formula (Tanino and Nepf, 2008) into the XBeach
non-hydrostatic wave model. These methodologies were tested using a series of
existing laboratory experiments involving nearshore vegetation on a sloping
beach. A key innovation of the study is the first application of metaheuristic
optimization algorithms for calibrating the drag coefficient, which enables
efficient automated searches to identify optimal values aligning with
measurements. We found that the optimization algorithms rapidly converge to
precise drag coefficients, enhancing accuracy and overcoming limitations in
manual calibration which can be laborious and inconsistent. While the
integrated empirical formula also demonstrates reasonable performance, the
optimization approach exemplifies the potential of computational techniques to
transform traditional practices of model calibration. Comparing these
strategies provides a framework to determine the most effective methodology
based on constraints in determining the vegetation drag coefficient.
