3-Dimensional direct sampling-based environmental contours using a semi-parametric joint probability model. (July 2021)

• Record Type: Journal Article
• Authors: Bai, Xiaoyu; Jiang, Hui; Huang, Xiaoyu; Song, Guangsong; Ma, Xinyi
• Is Part Of: Applied ocean research. Volume 112(2021)
• Publication Date: 2021-07
• Subjects: Log-transformed KDE-Paretotails -- Vine copula -- Environmental contours -- Direct sampling approach -- Importance sampling -- Ocean environmental variables; JPD joint probability distribution -- KDE kernel density estimation -- MCD marginal cumulative distributions -- CWW Coastal wind and wave -- GPD generalized Pareto distribution -- MLE maximum likelihood estimation -- PIT probability integral transformation -- ECP empirical copula process -- GOF Goodness of fit -- CvM Crámer–von Mises -- KS Kolmogorov–Smirnov; Ocean engineering -- Periodicals; 620.416205
• DOI: 10.1016/j.apor.2021.102710
• Languages: English
• ISSNs: 0141-1187

Abstract

Highlights: A log-transformed KDE-Paretotails approach proposed to fit marginal distributions. Joint distribution of ocean environmental variables estimated using vine copula. Importance sampling technic applied to keep Monte Carlo samples. Semi-parametric joint probability model proposed to construct 3-D environmental contours. 3-D environmental contours of ocean environmental variables analyzed. Abstract: Environmental contours are often utilized in response analysis and design of offshore structures. Properly modeling the multivariate joint probability distributions (JPD) of ocean environmental variables (e.g., wind speed, wave height, and wave period) is fundamental to constructing environmental contours. In this paper, we present an environmental contour construction procedure based on the direct sampling method. The procedure is a semi-parametric joint probability model designed to build the multivariate JPD of ocean environmental variables using a log-transformed kernel density estimation (KDE)-Paretotails approach to calculate statistical characteristics and estimate the marginal cumulative distributions (MCDs) and the vine copula using bivariate copulas as building blocks to fully define the complex dependence structures between MCDs. Furthermore, the importance sampling technique, which can easily be generalized to any dimension, was used to generate kept samples. The proposed model can accommodate three or higher dimensional direct sampling-based environmental contours and was shown by GOF tests to be well suited to building the multivariate JPDs of ocean environmental variables.