Evaluating probabilistic forecasts for maritime engineering operations

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
L. Astfalck, Michael Bertolacci, E. Cripps
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引用次数: 0

Abstract

Abstract Maritime engineering relies on model forecasts for many different processes, including meteorological and oceanographic forcings, structural responses, and energy demands. Understanding the performance and evaluation of such forecasting models is crucial in instilling reliability in maritime operations. Evaluation metrics that assess the point accuracy of the forecast (such as root-mean-squared error) are commonplace, but with the increased uptake of probabilistic forecasting methods such evaluation metrics may not consider the full forecasting distribution. The statistical theory of proper scoring rules provides a framework in which to score and compare competing probabilistic forecasts, but it is seldom appealed to in applications. This translational paper presents the underlying theory and principles of proper scoring rules, develops a simple panel of rules that may be used to robustly evaluate the performance of competing probabilistic forecasts, and demonstrates this with an application to forecasting surface winds at an asset on Australia’s North–West Shelf. Where appropriate, we relate the statistical theory to common requirements by maritime engineering industry. The case study is from a body of work that was undertaken to quantify the value resulting from an operational forecasting product and is a clear demonstration of the downstream impacts that statistical and data science methods can have in maritime engineering operations.
评估海事工程作业的概率预测
摘要海事工程依赖于许多不同过程的模型预测,包括气象和海洋学强迫、结构响应和能源需求。了解这种预测模型的性能和评估对于提高海上作业的可靠性至关重要。评估预测的点精度(如均方根误差)的评估度量是常见的,但随着概率预测方法的普及,这种评估度量可能不会考虑完整的预测分布。适当评分规则的统计理论提供了一个框架,在其中对竞争性概率预测进行评分和比较,但在应用中很少有吸引力。这篇转化论文介绍了适当评分规则的基本理论和原则,开发了一组简单的规则,可用于稳健地评估竞争概率预测的性能,并将其应用于澳大利亚西北陆架资产的地表风预测。在适当的情况下,我们将统计理论与海事工程行业的共同要求联系起来。该案例研究来自于为量化运营预测产品产生的价值而进行的一系列工作,清楚地表明了统计和数据科学方法在海事工程运营中可能产生的下游影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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