哈尔滨工程大学学报2026，Vol.47Issue(1)：12-19,8.DOI:10.11990/jheu.202404029
基于神经网络的Boussinesq模型在珊瑚礁地形波浪破碎判据确定方法
Neural network-based boussinesq model for determining wave breaking criteria over coral reef terrains
摘要
Abstract
The existing FUNWAVE-TVD model effectively simulates wave hydrodynamic changes in a flat terrain.How-ever,when simulating these changes in a coral reef terrain,its accuracy decreases because of an inaccurately set break-ing criterion.To overcome this limitation and accurately predict the optimal breaking criterion,an artificial neural net-work(NN)algorithm is introduced in this study.By exploring various acquired and preprocessed variables,an NN training dataset is constructed,and a corresponding backpropagation NN prediction model is designed.This model is subsequently used to predict the breaking criteria for four sets of regular waves acting on coral reef terrain.Applying these predictions to the FUNWAVE-TVD model,a comparative analysis of the simulated wave heights is performed against those generated using the default breaking criterion.The results reveal substantial improvement:the average goodness of fit(GoF)with measured wave heights for the four conditions is 0.8698,which is approximately 30%higher than the average GoF achieved using the default breaking criterion.Further analysis confirms the accuracy of this NN model:comparing the predicted breaking criteria for the four conditions against the empirically most suitable criteria re-veals the average relative error of only 0.0627 for the two datasets.This low error value definitely indicates that this NN model can effectively and reliably predict the optimal breaking criterion for the FUNWAVE-TVD model.关键词
FUNWAVE-TVD模型/破碎判据/人工神经网络/珊瑚礁地形/波浪水动力Key words
FUNWAVE-TVD model/breaking criterion/artificial neural network/coral reef terrain/wave hydrodynamics分类
海洋科学引用本文复制引用
姚旭,艾丛芳,张善举,马玉祥..基于神经网络的Boussinesq模型在珊瑚礁地形波浪破碎判据确定方法[J].哈尔滨工程大学学报,2026,47(1):12-19,8.基金项目
国家自然科学基金项目(52171248). （52171248）
