论文标题
基于森林分类器的随机流氓波浪对深海的预测
Random Forest Classifier Based Prediction of Rogue waves on Deep Oceans
论文作者
论文摘要
在本文中，我们提出了一种使用统计机器学习方法来预测海洋中流氓波的新方法。由于海洋由许多波系统组成，因此将从双峰或多模式定向分布变为单峰的变化被视为警告标准。同样，我们探索了有助于预测流氓波的各种功能。结果的分析表明，光谱特征在预测流氓波中很重要。我们发现非线性分类器比线性分类器具有更好的预测准确性。最后，我们提出了一种基于森林分类器的随机算法，以预测海洋条件下的流氓波。所提出的算法的总体准确度为89.57％至91.81％，根据预测时间窗口，平衡精度在79.41％至89.03％之间变化。此外，由于该方法的评估标准和跨学科特征的无模型性质，在其他非线性色散介质（例如非线性光学元件，等离子体和固体）中，可能会激励类似的研究，并受相似方程的控制，这将允许早期检测到极端波的早期检测
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
