Prediction of Wave Breaking Using Machine Learning Open Source Platform

摘要

A large number of studies on wave breaking have been carried out,and many experimental data have been documented.Moreover,on the basis of various experimental data set,many empirical or semi-empirical formulas based primarily on regression analysis have been proposed to quantitatively estimate wave breaking for engineering applications.However,wave breaking has an inherent variability,which imply that a linear statistical approach such as linear regression analysis might be inadequate.This study presents an alternative nonlinear method using an neural network,one of the machine learning methods,to estimate breaking wave height and breaking depth.The neural network is modeled using Tensorflow,a machine learning open source platform distributed by Google.The neural network is trained by randomly selecting the collected experimental data,and the trained neural network is evaluated using data not used for learning process.The results for wave breaking height and depth predicted by fully trained neural network are more accurate than those obtained by existing empirical formulas.These results show that neural network is an useful tool for the prediction of wave breaking.