Automated threshold selection methods for extreme wave analysis
Automated threshold selection methods for extreme wave analysis
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DOI：
10.1016/j.coastaleng.2009.06.003
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发表时间：
2009
影响因子：
4.4
通讯作者：
P. Thompson;Yuzhi Cai;D. Reeve;J. Stander
中科院分区：
文献类型：
--
作者：
P. Thompson;Yuzhi Cai;D. Reeve;J. Stander
波浪高等变量极值的研究在洪水风险评估和海岸设计中非常重要。通常，超过足够大阈值的值可以使用广义帕累托分布建模，其参数使用最大似然估计。有几种流行的经验技术可以选择合适的阈值，但这些都需要用户对图的主观解释。本文基于阈值变化时参数估计差的分布，提出了一种实用、自动化、简单且计算成本低廉的阈值选择方法，并将其应用于已公布的降雨量和新的波高数据集。我们通过使用自举过程评估与阈值选择技术相关的不确定性对回报水平估计的影响。我们通过模拟研究说明了我们的方法的有效性，并将其与JOINSEA软件中使用的方法进行了比较。此外，我们提出了一个扩展，允许选择的阈值取决于协变量的值，如波浪方向的余弦值。
The study of the extreme values of a variable such as wave height is very important in flood risk assessment and coastal design. Often values above a sufficiently large threshold can be modelled using the Generalized Pareto Distribution, the parameters of which are estimated using maximum likelihood. There are several popular empirical techniques for choosing a suitable threshold, but these require the subjective interpretation of plots by the user. In this paper we present a pragmatic automated, simple and computationally inexpensive threshold selection method based on the distribution of the difference of parameter estimates when the threshold is changed, and apply it to a published rainfall and a new wave height data set. We assess the effect of the uncertainty associated with our threshold selection technique on return level estimation by using the bootstrap procedure. We illustrate the effectiveness of our methodology by a simulation study and compare it with the approach used in the JOINSEA software. In addition, we present an extension that allows the threshold selected to depend on the value of a covariate such as the cosine of wave direction.