Forecasting Rogue Waves in Oceanic Waters

Abstract

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 highquality 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. The machine learning methods don't see future data during the training phase which helps remove data leakage. 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 30day 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.

Prediction of significant wave height based on EEMD and deep learning

“…Ali M (Ali et al, 2020) designed a machine learning model based on multiple linear regression and covariance-weighted least square estimation for forecasting near real-time SWH values within half an hour. Pokhrel P (Pokhrel et al, 2020) proposed a random forest classifier-based algorithm to predict anomalous ocean surges, which achieved an overall accuracy of 89.57% -91.81%. Memar S (Memar et al, 2021)applied two data-driven techniques, adaptive neuro-fuzzy inference system (ANFIS) and support vector regression (SVR), to predict the maximum seasonal wave height.…”