2020/03/13 by Pujan Pokhrel, Elias Ioup, Pokhrel, Pujan +7
Earth and Planetary Sciences · #Atmospheric and Oceanic Physics (physics.ao-ph) #Coastal and Marine Dynamics #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Ocean Waves and Remote Sensing #Oceanographic and Atmospheric Processes
paper · pdf · doi:10.48550/arxiv.2003.06431
openalex publication_date 2020/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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