  In this paper, we present a novel approach for the prediction of rogue waves
in oceans using statistical 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.
Likewise, 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. We find that nonlinear classifiers have better
prediction accuracy than the linear ones. Finally, we propose a Random Forest
Classifier based algorithm to predict rogue waves in oceanic conditions. The
proposed algorithm has an Overall Accuracy of 89.57% to 91.81%, and the
Balanced Accuracy varies between 79.41% to 89.03% depending on the forecast
time window. Moreover, due to the model-free nature of the evaluation criteria
and interdisciplinary characteristics of the approach, similar studies may be
motivated in other nonlinear dispersive media, such as nonlinear optics,
plasma, and solids, governed by similar equations, which will allow for the
early detection of extreme waves
