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In this paper, we propose a Light Gradient Boosting (LightGBM) to forecastdominant wave periods in oceanic waters. First, we use the data collected fromCDIP buoys and apply various data filtering methods. The data filtering methodsallow us to obtain a high-quality dataset for training and validation purposes.We then extract various wave-based features like wave heights, periods,skewness, kurtosis, etc., and atmospheric features like humidity, pressure, andair temperature for the buoys. Afterward, we train algorithms that use LightGBMand Extra Trees through a hv-block cross-validation scheme to forecast dominantwave periods for up to 30 days ahead. LightGBM has the R2 score of 0.94, 0.94,and 0.94 for 1-day ahead, 15-day ahead, and 30-day ahead prediction. Similarly,Extra Trees (ET) has an R2 score of 0.88, 0.86, and 0.85 for 1-day ahead,15-day ahead, and 30 day ahead prediction. In case of the test dataset,LightGBM has R2 score of 0.94, 0.94, and 0.94 for 1-day ahead, 15-day ahead and30-day ahead prediction. ET has R2 score of 0.88, 0.86, and 0.85 for 1-dayahead, 15-day ahead, and 30-day ahead prediction. A similar R2 score for bothtraining and the test dataset suggests that the machine learning modelsdeveloped in this paper are robust. Since the LightGBM algorithm outperforms ETfor all the windows tested, it is taken as the final algorithm. Note that theperformance of both methods does not decrease significantly as the forecasthorizon increases. Likewise, the proposed method outperforms the numericalapproaches included in this paper in the test dataset. For 1 day aheadprediction, the proposed algorithm has SI, Bias, CC, and RMSE of 0.09, 0.00,0.97, and 1.78 compared to 0.268, 0.40, 0.63, and 2.18 for the European Centrefor Medium-range Weather Forecasts (ECMWF) model, which outperforms all theother methods in the test dataset.

A LightGBM based Forecasting of Dominant Wave Periods in Oceanic Waters