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Neural Networks Based Simulation of Significant Wave Height

[+] Author Affiliations
H. Bazargan, A. Bazargan

Shahid Bahonar University of Kerman, Kerman, Kerman, Iran

H. Bahai

Brunel University, Uxbridge, England, UK

A. Aminzadeh-Gohari

University of California at Berkeley, Berkeley, CA

Paper No. OMAE2007-29171, pp. 401-409; 9 pages
  • ASME 2007 26th International Conference on Offshore Mechanics and Arctic Engineering
  • Volume 4: Materials Technology; Ocean Engineering
  • San Diego, California, USA, June 10–15, 2007
  • Conference Sponsors: Ocean, Offshore and Arctic Engineering Division
  • ISBN: 0-7918-4270-3 | eISBN: 0-7918-3799-8
  • Copyright © 2007 by ASME


A large number of ocean activities call for real time or on-line forecasting of wind wave characteristics including significant wave height (Hs). The work reported in this paper uses statistics, and artificial neural networks trained with an optimization technique called simulated annealing to estimate the parameters of a probability distribution called hepta-parameter spline for the conditional probability density functions (pdf’s) of significant wave heights given their eight immediate preceding 3-hourly observed Hs’s. These pdf’s are used in the simulation of significant wave heights related to a location in the Pacific. The paper also deals with short and long term forecasting of Hs for the region through generating random variates from the spline distribution.

Copyright © 2007 by ASME



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