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Data article: Genetic algorithm model and data files to assess JONSWAP spectra coefficients: MATLAB code

Manuscript title: Genetic algorithm model and data files to assess JONSWAP spectra coefficients: MATLAB code

Authors: Rueda-Bayona, J.G., Guzmán, A

Data in Brief (ISSN: 2352-3409). Available online: August 19, 2020. 106196 (In press). https://doi.org/10.1016/j.dib.2020.106196  

Abstract: This data article presents the structure of a Genetic Algorithm model written in a MATLAB code for finding the 1D JONSWAP spectra parameters when measured raw spectra is not available. The JONSWAP spectra is widely used in Coastal, Offshore, and Ocean Engineering for determining wave parameters for structure designing and numerical modeling. However, finding proper spectra parameters may be difficult because of the limitations of parameterized equations to do so and the high non-linear relation between the alpha and gamma coefficients. This GA model can find the alpha and gamma parameters for specific locations considering sea-state's evolution and water-depth transitions. The application of the GA model of this data article is shown in Rueda-Bayona et al.

Keywords: Genetic algorithms; JONSWAP; numerical modeling; waves; MATLAB

Research article: Genetic algorithms to determine JONSWAP spectra parameters

Manuscript title: Genetic algorithms to determine JONSWAP spectra parameters

Authors: Juan Gabriel Rueda-Bayona, Andrés Guzmán, Rodolfo Silva

Ocean Dynamics. January 09 2020. https://doi.org/10.1007/s10236-019-01341-8

Abstract: The genetic algorithm (GA) model presented here provides specific JONSWAP parameters that can be used for wave modelling. This work describes a validated heuristic model based on GA, to select JONSWAP spectra parameters, regardless of water depth restrictions and sea state conditions. The identification of the JONSWAP spectra parameters is difficult, as the alpha and gamma coefficients have scattered distributions that modulate the spectral peak energy. In addition, the selection of alpha and gamma coefficients from in situ free surface records may be difficult and time-consuming, because of the amount of data and nonlinearities involved. The proposed model uses either in situ or numerically modelled wave data and has three main steps: (1) generation and crossover, (2) minimisation of the cost function ΔHs, defined as the minimum difference between the calculated artificial significant wave height and the in situ wave height (instrumented or modelled), and (3) mutation and natural selection. To apply the model, in situ wave data measured by an acoustic Doppler current profiler over 5.5 months was used in this research. The results show a high correlation (r^2), of 0.95, between the best fitted curves of modelled spectra and measured data.

Keywords: Genetic algorithm; JONSWAP; parameters; spectral peak energy