A LightGBM based Forecasting of Dominant Wave Periods in Oceanic Waters. (arXiv:2105.08721v1 [physics.ao-ph])

In this paper, we propose a Light Gradient Boosting (LightGBM) to forecast
dominant wave periods in oceanic waters. First, we use the data collected from
CDIP buoys and apply various data filtering methods. The data filtering methods
allow 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, and
air temperature for the buoys. Afterward, we train algorithms that use LightGBM
and Extra Trees through a hv-block cross-validation scheme to forecast dominant
wave 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 and
30-day ahead prediction. ET has R2 score of 0.88, 0.86, and 0.85 for 1-day
ahead, 15-day ahead, and 30-day ahead prediction. A similar R2 score for both
training and the test dataset suggests that the machine learning models
developed in this paper are robust. Since the LightGBM algorithm outperforms ET
for all the windows tested, it is taken as the final algorithm. Note that the
performance of both methods does not decrease significantly as the forecast
horizon increases. Likewise, the proposed method outperforms the numerical
approaches included in this paper in the test dataset. For 1 day ahead
prediction, 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 Centre
for Medium-range Weather Forecasts (ECMWF) model, which outperforms all the
other methods in the test dataset.

Source: https://arxiv.org/abs/2105.08721

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