Going beyond modifying the SPM method and comparing it with the intelligent GIDS model

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Ahmadvand Homayoon; Najarpoor Mohammad Ali; Akbarinasab Mohammad; Esmaili Paeen Afrakoti Iman

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Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF MARINE TECHNOLOGY Year:2023 | Volume: | Issue: Page(s): 73-90

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Title

Going beyond modifying the SPM method and comparing it with the intelligent GIDS model

Author(s)

Ahmadvand Homayoon | Najarpoor Mohammad Ali | Akbarinasab Mohammad | Esmaili Paeen Afrakoti Iman | Issue Writer Certificate

Keywords

Wave Height PredictionQ4

Gulf of MexicoQ4

SPM MethodQ4

CEM MethodQ4

GIDS ModelQ4

genetic algorithmQ4

Abstract

In recent decades, different methods of wave prediction model have been used. Experimental methods, numerical methods, soft computing algorithms are among such methods. In this study, the height of the waves in the Gulf of Mexico is predicted in two different sections. In the first part, the ALM algorithm and GIDS Model and in the second part, the experimental methods of SPM and CEM are investigated. For this purpose, first, the collected data and wind stress factor in SPM model were pre-processed using ALM and GIDS Models. After investigations, it was determined that the wind stress factor should be corrected with a dimensionless correction factor p⁄((1⁄2 ρ〖〖 u〗_10〗^2 ) ). In the next step, the most suitable input of the GIDS Model was selected and in order to reduce the time of this modeling, different combinations of modified wind stress factor Ua with wavelength were considered, which resulted in the parameter U_a (gX)^(-0.02) The most appropriate input for the GIDS Model is in predicting the height of the waves. In the second part of the paper, each of the SPM and CEM models was implemented and compared with the optimized GIDS Model. The results showed that the GIDS Model was more accurate in predicting the wave height of the Gulf of Mexico. Then the wind speed correction coefficient was modified using a genetic algorithm and with this operation the SPM model was modified and presented as the most appropriate prediction model.

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