Simulation of the mean zero-up-crossing wave period using artificial neural networks trained with a simulated annealing algorithm |
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Authors: | Hamid Bazargan Hamid Bahai Farzad Aryana |
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Institution: | (1) College of Engineering, Shahid Bahonar University of Kerman, Iran;(2) School of Engineering and Design, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK |
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Abstract: | The aim of this work was to develop a predictive model to forecast the mean zero-up-crossing wave periods (T
z
) for 3-hourly sea states at a location in the Pacific using artificial neural networks (ANNs). Seven multilayer ANNs were
trained with a simulated annealing algorithm. The output of each trained ANN was used to estimate each of the seven parameters
of a new distribution called the hepta-parameter spline proposed for the conditional distribution of T
z
, given some mean zero-up-crossing wave periods and significant wave heights. After estimating the parameters of the distribution,
the model was used to simulate and predict future values of T
z
. Forecasting a sea state and developing the joint distribution of sea state characteristics with the help of the simulated
characteristics are also discussed in this article. |
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Keywords: | Mean zero-up-crossing wave period Simulation Neural networks Simulated annealing Hepta-parameter spline distribution |
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