Variation of ln(q<sub>pred</sub>) with FD.

<div><p>Accurate prediction of mean wave overtopping discharge is essential for the safe and cost-effective design of coastal defence structures. While traditional empirical, physical, and numerical models remain important, Machine Learning (ML) has recently emerged as a powerful complem...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	M. A. Habib (16839161) (author)
مؤلفون آخرون:	S. Abolfathi (16839167) (author), J. J. O’Sullivan (16839164) (author), M. Salauddin (10017388) (author)
منشور في:	2025
الموضوعات:	Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified influential parameters across gaussian process regression coastal defence structures improved predictive formulae best predictive performance practical engineering needs powerful complementary tool coastal infrastructure design respectively ), indicating five ml algorithms div >< p >) based solely predictive accuracy reliable tool practical usability effective design >) within svr ), gbdt ), work advances wave overtopping validated using traditional empirical three kernel study presents structured pre strong agreement sloped breakwaters simplified equations relative freeboard recently emerged rae values q </ processing steps observed data new set model interpretability ml findings machine learning lowest rmse ln (< gpr yielded gpr ). gp ). genetic programming freeboard deficit enhance interpretability effectively modelling driven techniques based models
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Variation of ln(q<sub>pred</sub>) with FD.

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