https://nova.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:39866 Q_b) is a key parameter for parametric surf zone models. It is via this variable that these models control the energy dissipation in the surf zone. Historically, Q_b has been obtained using probability distribution functions (PDFs) of the wave height (p(H)). This paper describes an alternative, data-driven approach to obtaining the fraction of broken waves that is a significant improvement over the more traditional approaches. This new model is based on an ensemble of regression trees in which Q_b is learnt directly from an extensive field dataset. The ensemble uses three input parameters that are often available to coastal engineers: offshore significant wave height (𝐻_𝑚0∞), offshore peak wave period (𝑇_𝑚01∞), and time-averaged relative water depths relative to the mean sea level (h/𝐻_𝑚0∞), and predicts Q_b at an averaged given relative water depth. The results indicate that the model can predict the depth-dependent variability of Q_b with a high degree of accuracy (averaged r² ≥ 0.95, averaged root mean square error ≤ 0.05, averaged mean absolute error ≤ 0.04) in virtually no computational time. When compared to three widely used Q_b models that are derived from PDFs of the wave heights, the model developed here showed significant improvement with reductions in the errors (average error reduction of 25%) and significant improvement for r²-scores (average increase ≥ 30%). Although complex, the method developed here could be advantageous over the more traditional approach because of its high degree of precision and accuracy and because it does not depend on prior knowledge of p(H). In summary, the present model could be used as a replacement for the formulation of Q_b in parametric wave models, which should result in better overall predictions, and thus, in better coastal management tools.]]> Thu 21 Jul 2022 09:34:35 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:39858 Thu 21 Jul 2022 09:34:26 AEST ]]>