Significant wave height forecasting is a key problem in ocean data analytics. Predicting the significant wave height is crucial for estimating the energy production from waves. Moreover, the timely prediction of large waves is important to ensure the safety of maritime operations, e.g. passage of vessels. We frame the task of predicting extreme values of significant wave height as an exceedance probability forecasting problem. Accordingly, we aim at estimating the probability that the significant wave height will exceed a predefined threshold. This task is usually solved using a probabilistic binary classification model. Instead, we propose a novel approach based on a forecasting model. The method leverages the forecasts for the upcoming observations to estimate the exceedance probability according to the cumulative distribution function. We carried out experiments using data from a buoy placed in the coast of Halifax, Canada. The results suggest that the proposed methodology is better than state-of-the-art approaches for exceedance probability forecasting.

翻译：有效波高预测是海洋数据分析中的关键问题。预测有效波高对于估算波浪能发电量至关重要。此外，对巨浪的及时预测对于保障海上作业（如船舶通航）的安全具有重要意义。本文将有效波高极值预测任务构建为超阈值概率预测问题，旨在估计有效波高超过预设阈值的概率。此类任务通常采用概率二元分类模型求解。本文提出了一种基于预测模型的新方法，通过利用未来观测值的预测结果，根据累积分布函数估算超阈值概率。使用加拿大哈利法克斯海岸浮标数据进行实验，结果表明，所提方法在超阈值概率预测方面优于现有最先进方法。