Prediction of Wave Transmission Characteristics of Low Crested Structures Using Artificial Neural Network

Abstract

As climate change causes sea levels to rise, which increases the external forces of high waves, shoreline deformation with coastal erosion and scour has received significant attention, becoming an important social issue in many countries. In particular, morphological changes in seabed due to such coastal erosion and sedimentation can cause changes in the coastal environment and ecosystems. Various deformation methods have been proposed to recover coastal erosion issues, but commercially utilized gravity-type structures such as breakwaters and headlands change the sea environment, resulting in bad seawater circulation and poor water quality. Low-crested and submerged structures (LCS), such as detached breakwater and artificial reefs, diminish the wave height with reduced wave energy behind a structure due to the change in the freeboard at the still water surface, which protects the inland sea environment. The construction of LCS is performed under specific conditions to produce the desired wave transmission coefficient; thus, the calculation or prediction of the transmission coefficient of the structure should be carried out as an important factor in designing the structure. To determine the wave transmission coefficient of LCS, various studies have proposed formulas for calculating the wave transmission rate, but the wave transmission coefficients are estimated through a regression analysis of mathematical experimental data, showing a limited analysis of the natural phenomena (Koosheh et al., 2020;Formentin et al., 2017).Recently, a machine learning model has been used to estimate and predict statistical structures from input and output data. This machine learning model can readily explain the regression analysis of nonlinear relationships (Hashemi et al., 2010;Rigos et al., 2016). Machinelearning-based prediction models employ deep learning algorithms of neural networks, which have been continuously applied in the field of coastal engineering in recent years, especially for solving problems