Beyond the data range approach to soft compute the reflection coefficient for emerged perforated semicircular breakwater

Abstract

Prediction of reflection coefficient (K<inf>r</inf>) for emerged perforated semicircular breakwater (EPSBW) using artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) is carried out in the present paper. A new approach has been adopted in the present work using ANN and ANFIS models for the prediction of the reflection coefficient (K<inf>r</inf>) for the wave periods beyond the range of the dataset used for training the network. The experimental data obtained for a scaled down EPSBW model from regular wave flume experiments at Marine Structure laboratory of National Institute of Technology Karnataka, Surathkal, Mangaluru, India was used. The ensemble was segregated such that certain higher ranges of wave periods were excluded in the training, and possibility of prediction was checked. The independent input parameters (H<inf>i</inf>, T, S, D, R, d, h<inf>s</inf>) that influence the reflection coefficient (K<inf>r</inf>) are considered for training as well as testing, where H<inf>i</inf> is the incident wave height, T is the wave period, S is the spacing of perforations, D is the diameter of the perforations, R is the radius of the breakwater, d is the depth of the water and h<inf>s</inf> is the structure height. The accuracy of predictions of reflection coefficient (K<inf>r</inf>) is done based on the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The study shows that ANN and ANFIS models may be used for prediction of reflection coefficient K<inf>r</inf> of semicircular breakwater for beyond the data range of wave periods used for training. However, ANFIS outperformed ANN model in the prediction of K<inf>r</inf> in the case of beyond the data range segregation method. © Springer Nature Singapore Pte Ltd. 2019.