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| Main Authors: | , |
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| Format: | Preprint |
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2022
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| Online Access: | https://arxiv.org/abs/2206.09821 |
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| _version_ | 1866913341969006592 |
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| author | Cerqueira, Vitor Torgo, Luis |
| author_facet | Cerqueira, Vitor Torgo, Luis |
| contents | Significant wave height forecasting is a key problem in ocean data analytics. This task affects 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 or an ensemble of forecasts. Instead, we propose a novel approach based on point forecasting. Computing both type of forecasts (binary probabilities and point forecasts) can be useful for decision-makers. While a probabilistic binary forecast streamlines information for end-users concerning exceedance events, the point forecasts can provide additional insights into the upcoming future dynamics. The procedure of the proposed solution works by assuming that the point forecasts follow a distribution with the location parameter equal to that forecast. Then, we convert these point forecasts into exceedance probability estimates using the cumulative distribution function. We carried out experiments using data from a smart 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_09821 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Exceedance Probability Forecasting via Regression for Significant Wave Height Prediction Cerqueira, Vitor Torgo, Luis Machine Learning Significant wave height forecasting is a key problem in ocean data analytics. This task affects 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 or an ensemble of forecasts. Instead, we propose a novel approach based on point forecasting. Computing both type of forecasts (binary probabilities and point forecasts) can be useful for decision-makers. While a probabilistic binary forecast streamlines information for end-users concerning exceedance events, the point forecasts can provide additional insights into the upcoming future dynamics. The procedure of the proposed solution works by assuming that the point forecasts follow a distribution with the location parameter equal to that forecast. Then, we convert these point forecasts into exceedance probability estimates using the cumulative distribution function. We carried out experiments using data from a smart 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. |
| title | Exceedance Probability Forecasting via Regression for Significant Wave Height Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2206.09821 |