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Joint probability distribution model of wind and wave with Vine Copula function
- Journal of Civil and Environmental Engineering Vol. 45, Issue 4, Pages: 83-93(2023)
- 作者机构：
西南交通大学 桥梁工程系，成都 610031
- 作者简介：
brief: WANG Wang (1994- ), main research interest: coupled wind-wave-bridge dynamics of long-span bridges, E-mail: wangwang@my.swjtu.edu.cn.
ZHU Jin （corresponding author）, associate research fellow, postdoctor, E-mail: zhujin@home.swjtu.edu.cn.
- 基金信息：National Natural Science Foundation of China(51908472);Project of Science and Technology Department of Sichuan Province(2020YJ0080);China Postdoctoral Science Foundation(2019M663554;2019TQ0271)
DOI：10.11835/j.issn.2096-6717.2021.148
CLC： U441.2Received：06 May 2021，
Published：25 August 2023
- 稿件说明：
移动端阅览
WANG Wang, ZHU Jin, KANG Rui, et al. Joint probability distribution model of wind and wave with Vine Copula function[J]. Journal of Civil and Environmental Engineering, 2023, 45(4): 83-93.
WANG Wang, ZHU Jin, KANG Rui, et al. Joint probability distribution model of wind and wave with Vine Copula function[J]. Journal of Civil and Environmental Engineering, 2023, 45(4): 83-93. DOI： 10.11835/j.issn.2096-6717.2021.148.
摘要
随着全球气候变暖的加剧，极端气候现象发生的频率和强度均可能加大，这对海岸和近海结构的安全不利。基于中国东海的连云港海洋观测站实测风浪数据和Vine Copula理论，建立风浪要素中风速、波高、波浪周期、风向和波向五维随机变量之间的联合概率分布模型。采用极大似然法确定各风浪要素边缘分布模型参数，通过AIC信息准则和均方根误差RMSE进行拟合优度评价，由此建立风浪要素的边缘分布。采用带有基于残差的高斯似然函数的贝叶斯框架估计二维Copula函数的参数，结合AIC信息准则进行拟合优度评价并确定最优Copula函数。绘制最优联合分布概率密度图，与二维频率直方图进行对比以评价模型效果。采用Vine Copula函数建立多维联合概率模型并结合AIC值评价其拟合优度。研究结果表明：建立的Vine Copula模型可以较好地刻画风速、波高、波浪周期、风向和波向五维随机变量之间的联合概率分布。
Abstract
With the intensification of global climate warming
the probabilities and load intensities of extreme weather phenomenon are gradually increasing
which could threaten the safety of coastal and offshore infrastructures. The present study presents a joint probability distribution model of wind speed
wave height
wave period
wind direction and wave direction with Vine Copula function based on monitoring data from Lianyungang Ocean Station in the East China Sea. Firstly
the marginal probability distributions of wind and wave data are determined
in which the AIC criteria and RMSE index are employed to select the optimal probability distribution model and the maximum likelihood method is used to determine the model parameters. Subsequently
the optimal two-dimensional Copula function for wind and wave data is determined via the AIC criteria
and the model parameters are fitted with a Bayesian framework with a residual-based Gaussian likelihood function. To illustrate the goodness of fit
the binary frequency histogram of the original wind and wave data is compared with the proposed two-dimensional Copula function. Finally
the multi-dimensional joint probability distribution model of wind and wave data is established with the Vine Copula function based on the AIC criteria. The results show that the proposed Vine Copula model is able to describe the joint probability distribution between the wind speed
wave height
wave period
wind direction and wave direction.
关键词
Keywords
references
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