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Volume 7, Issue 14 (Autumn and Winter 2012 2012)                   Marine Engineering 2012, 7(14): 117-123 | Back to browse issues page

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nemati M, Karami Khaniki A. Online Significant Wave Height Prediction in Persian Gulf Using Artificial Neural Networks and Regression Trees. Marine Engineering 2012; 7 (14) :117-123
URL: http://marine-eng.ir/article-1-193-en.html
Online Significant Wave Height Prediction in Persian Gulf Using Artificial Neural Networks and Regression Trees
Maryam Nemati * 1, Ali Karami Khaniki2
1- Center for Science and Research, I. A. University
2- Research Center for Soil Protection and Water Resources
Abstract:   (9293 Views)
Prediction of wave height is of great importance in marine and coastal engineering. In this study, the performances of artificial neural networks (feed forward with back propagation algorithm) for online significant wave heights prediction, in Persian Gulf, were investigated. The data set used in this study comprises wave and wind data gathered from shallow water location in Persian Gulf. Current wind speed (u) and those belonging up to eight previous hours are given as input variables, while the significant wave height with leading time of 1-24 hour are the output parameters. Results show that the artificial neural networks can perform very well in predicting significant wave height, when shorter intervals of predictions (6 hour) were involved. Small interval predictions were made more accurately than the large interval ones. Results of artificial neural networks were compared with those of regression trees. Results indicate that error statistics of neural networks and regression trees were nearly similar
Keywords: Prediction, wave, Artificial Neural Networks, Regression Trees, Persian Gulf, Asaluye
Full-Text [PDF 241 kb]   (2712 Downloads)    
Type of Study: Technical Note | Subject: Environmental Study
Received: 2012/06/11 | Accepted: 2013/10/19
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International Journal of Maritime Technology is licensed under a

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