Exceedance Probability Forecasting via Regression for Significant Wave Height Prediction
arxiv(2022)
摘要
Significant wave height forecasting is a key problem in ocean data analytics.
This problem is relevant in several maritime operations, such as managing the
passage of vessels or estimating the energy production from waves. In this
work, we focus on the prediction of extreme values of significant wave height
that can cause coastal disasters. This task is framed as an exceedance
probability forecasting problem. Accordingly, we aim to estimate the
probability that the significant wave height will exceed a predefined critical
threshold. This problem is usually solved using a probabilistic binary
classification model. Instead, we propose a novel approach based on a
forecasting model. A probabilistic binary forecast streamlines information for
decision-making, and point forecasts can provide additional insights into the
data dynamics. The proposed method works by converting point forecasts into
exceedance probability estimates using the cumulative distribution function. We
carried out experiments using data from a buoy placed on the coast of Halifax,
Canada. The results suggest that the proposed methodology is better than
state-of-the-art approaches for exceedance probability forecasting.
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