Forecasting Rogue Waves in Oceanic Waters

We present a novel approach for the prediction of Rogue Waves in oceans using Machine Learning methods. Since the ocean is composed of many wave systems, the change from a bimodal or multimodal directional distribution to unimodal one is taken as the warning criteria. Further, we explore various features that help in predicting Rogue Waves. The analysis of the results shows that the Spectral features are significant in predicting Rogue Waves. Finally, we propose a Random Forest Classifier based algorithm to predict Rogue Waves in oceanic conditions. For a range of windows, the proposed algorithm has accuracies between 89.57% and 91.81%, and the balanced accuracies between 79.41% and 89.03%. Moreover, we have also introduced the publicly available buoy dataset, which can serve as a benchmark dataset.