English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

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.

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