Abstract

This study presents a comprehensive, evidence-based methodological framework for estimating extreme significant wave heights associated with several return periods using meteorological records (wind speed and air temperature) in combination with machine learning algorithms. The proposed framework integrates a sequence of preliminary diagnostic analyses prior to extreme value modelling, including homogeneity tests to identify potential change points and trend analyses to assess the presence of stationary or non-stationary behaviour, thereby enabling the selection of an appropriate frequency analysis approach for computing annual maximum significant wave heights across several return periods. The methodology is applied to annual maximum significant wave height records obtained from offshore buoy stations in the Antilles region of the Caribbean Sea. Results from the machine learning presets demonstrate a strong relationship between extreme significant wave heights and the corresponding wind speed and temperature records, yielding coefficients of determination (R2) of 0.75 and 0.88 for the validation and testing stages, respectively. When frequency analysis is conducted using both the traditional approach and the proposed machine learning–based methodology, high agreement is observed for return periods between 2 and 20 years, with R2 values ranging from 0.98 to 0.89. For longer return periods, the agreement decreases, which is consistent with the limited length of the available wave height records and the supervised nature of the machine learning algorithms. In this sense, the proposed framework provides a robust alternative for estimating extreme significant wave heights in regions where direct wave observations are unavailable but meteorological records are accessible.

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