Scientia Iranica

Scientia Iranica

Application of Neural Network Models to Improve Prediction Accuracy of Wave Run-up on Antifer Covered Breakwater

Authors
A. Rabiei
A. Najafi-Jilani
A. Najafi-Jilani
M. Zakeri-Niri
Department of Civil Engineering, Islamic Azad University, Islamshahr Branch, Islamshahr, Iran.
10.24200/sci.2017.2418
Abstract
The primary goal of this study is to present a better way in terms of cost and experimenting duration, instead of using experimental ways for investigating the wave run-up (Ru) over rubble-mound breakwater and examining the effect of placement pattern of antifer units on the amount of wave run-up. To do so, artificial neural networks (ANNs) are suggested. For the sake of comparison, the proposed modeling is put into contrast by the ones obtained via other approaches in the literature. The Multi-Layer Perceptron (MLP) is selected as the artificial neural network exerted in this study. In the designed neural network, the numbers of inputs and outputs are selected as four and one, respectively. On the other hand, the number of neurons in the single hidden layer of the network should be determined by trial and error considering the Mean Square Error (MSE) of the training and validation samples, which has been chosen as seven in this paper. The regression equations and MSE for the results obtained by ANN have presented in this paper and are compared with other models in the literature. Moreover, the regular placement is preferred to other placement patterns due to its less MSE obtained by ANN.
Keywords
wave run-up
Breakwater
artificial neural network
Multi-layer Perceptron (MLP)
MSE
Scientia Iranica
Volume 24, Issue 2 - Serial Number 2
Transactions on Civil Engineering (A)
March and April 2017
Pages 567-575

  • Receive Date 26 April 2016
  • Accept Date 09 July 2017