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Detection and prediction of segments containing extreme significant wave heights

机译：检测和预测包含极高波高的线段

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This paper presents a methodology for the detection and prediction of Segments containing very high Significant Wave Height (SSWH) values in oceans. This kind of prediction is needed in order to account for potential changes in a long-term future operational environment of marine and coastal structures. The methodology firstly characterizes the wave height time series by approximating it using a sequence of labeled segments, and then a binary classifier is trained to predict the occurrence of SSWH periods based on past height values. A genetic algorithm (GA) combined with a likelihood-based local search is proposed for the first stage (detection), and the second stage (prediction) is tackled by an Artificial Neural Network (ANN) trained with a Multiobjective Evolutionary Algorithm (MOEA). Given the unbalanced nature of the dataset (SSWH are rarer than non SSWH), the MOEA is specifically designed to obtain a balance between global accuracy and individual sensitivities for both classes. The results obtained show that the GA is able to group SSWH in a specific cluster of segments and that the MOEA obtains ANN models able to perform an acceptable prediction of these SSWH.
机译：本文提出了一种检测和预测海洋中包含非常高的显着波高（SSWH）值的航段的方法。为了考虑到海洋和沿海结构的长期未来运行环境中的潜在变化，需要进行这种预测。该方法首先通过使用一系列标记段将波高时间序列逼近来表征波高时间序列，然后训练二元分类器以基于过去的高度值预测SSWH周期的发生。针对第一阶段（检测），提出了一种结合了基于似然性的局部搜索的遗传算法（GA），第二阶段（预测）则是由经过多目标进化算法（MOEA）训练的人工神经网络（ANN）解决的。考虑到数据集的不平衡性质（SSWH比非SSWH稀有），MOEA专门设计用于在两种类别的全局精度和个体敏感性之间取得平衡。获得的结果表明，GA能够将SSWH分组为特定的段群，而MOEA获得的ANN模型能够对这些SSWH进行可接受的预测。

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