Multivariate environmental contours using C-vine copulas

Montes-Iturrizaga, R.; Heredia‐Zavoni, Ernesto

doi:10.1016/j.oceaneng.2016.03.011

Cited by 62 publications

(15 citation statements)

References 28 publications

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“…In this article, proper description of these asymmetries and especially that of the upper tail dependence is crucial for offshore operations because these are mainly influenced by extreme environmental conditions. Previous studies on the application of vine copulas for describing sea states have already showed the relevance of modelling the dependence between wind and wave conditions (Montes-Iturrizaga & Heredia-Zavoni, 2016;Leontaris et al, 2016;Jäger & Nápoles, 2017). This article extends these studies including the tidal velocity as an extra important environmental condition parameter.…”

Section: Introductionmentioning

confidence: 62%

“…The individual nodes are labeled as v(i, j) to correspond with the mathematical simulation algorithm, as is described in . It should be noted that using vine copulas to simulate synthetic time series requires the input dataset to be time independent and identically distributed Naevestad, 2009;Montes-Iturrizaga & Heredia-Zavoni, 2016), since each set of samples is generated independently from previous simulations. In an offshore application regarding tidal infrastructure, due to the diurnal recurring character of the tidal currents, the required time interval between time samples has shown to be too small to obtain the aforementioned time independence.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Offshore infrastructure planning using a vine copula approach for environmental conditions: an application for replacement maintenance of tidal energy infrastructure

Nie

Leontaris

Hoogendoorn

et al. 2019

Structure and Infrastructure Engineering

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Installation and maintenance operations of offshore assets are impacted by local environmental conditions such as wave height and period, wind speed and current velocity. These parameters are substantially of influence for the asset planning (time and costs) given the uncertainty of operational windows. In this article, a method is proposed to construct realistic time series of the aforementioned dependent conditions using a vine copulas approach. This method makes it possible to obtain a large number of realizations of these conditions at a certain location. It is shown that the operational windows remain persistent with the original limited dataset. Moreover, this method enables the incorporation of environmental uncertainties in the operational planning processes. To illustrate the value of this method, an application regarding replacement maintenance of a tidal energy infrastructure is examined. For this purpose, the maintenance activities are represented as a semi-Markov decision process. For every synthetic environmental time series, the algorithm finds the optimal set of decisions and the corresponding maintenance plans, including replacement costs and revenue losses. It is shown that the proposed method is effective in replacement maintenance decision making, while taking into account the environmental uncertainties.

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Section: Introductionmentioning

confidence: 62%

Section: Theoretical Backgroundmentioning

confidence: 99%

Offshore infrastructure planning using a vine copula approach for environmental conditions: an application for replacement maintenance of tidal energy infrastructure

Nie

Leontaris

Hoogendoorn

et al. 2019

Structure and Infrastructure Engineering

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“…This would require the use of standard multivariate copulas or vine copulas as a way to construct multivariate distributions (see Nelsen, 2006;Montes-Iturrizaga et al, 2016) and performing certain comparisons.…”

Section: Discussionmentioning

confidence: 99%

Sales Comparison Approach Indicating Heterogeneity of Particular Type of Real Estate and Corresponding Valuation Accuracy

Cupal¹

2017

Acta Univ. Agric. Silvic. Mendelianae Brun.

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The article focuses on heterogeneity of goods, namely real estate and consequently deals with market valuation accuracy. The heterogeneity of real estate property is, in particular, that every unit is unique in terms of its construction, condition, financing and mainly location and thus assessing the value must necessarily be difficult. This research also indicates the rate of efficiency of markets across the types based on their level of variability. The research is based on two databases consisting of various types of real estate with specific market parameters. These parameters determine the differences across the types and reveal heterogeneity. The first database has been set on valuations by sales comparison approach and the second one on data of real properties offered on the market. The methodology is based on univariate and multivariate statistics of key variables of those databases. The multivariate analysis is performed by Hotelling T 2 control chart and statistics with appropriate numerical characteristics. The results of both databases were joint by weights with regard to the dependence criterion of the variables. The final results indicate potential valuation accuracy across the types. The main contribution of the research is that the evaluation was not only derived from the price deviation or distribution, but it also draws from causes of real property heterogeneity as a whole.

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“…For instance, the elliptical family has Gaussian and Student t copulas, while the Archimedean family would have copulas such as Gumbel, Frank, and Joe. Different copulas have been employed to quantify the risks of compound extremes (Corbella & Stretch, 2012; Li et al., 2018; Montes‐Iturrizaga & Heredia‐Zavoni, 2015), and the selection of the copula function may significantly influence the resulting risks or environmental contours (Montes‐Iturrizaga & Heredia‐Zavoni, 2016). In this study, the Frank, Joe, and Gumbel copulas, as presented in Table S2 in Supporting Information S1, were considered as the candidate models for quantifying the interdependence of compound extremes.…”

Section: Methodsmentioning

confidence: 99%

Tracing Uncertainty Contributors in the Multi‐Hazard Risk Analysis for Compound Extremes

Fan

Shi

et al. 2021

Earth's Future

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In this study, an iterative factorial multimodel Bayesian copula (IFMBC) framework was developed for revealing uncertainties in risk inferences of compound extremes under the consideration of diverse model structures and parameter sets. Particularly, an iterative factorial analysis (IFA) method would be advanced in IFMBC to track the dominant contributors to the imprecise predictions of multi‐hazard risks. The developed IFMBC framework was applied for the risk assessment of compound floods at two estuarine systems (i.e., Washington and Philadelphia) in US. The results indicate that the most likely compound events, under predefined return periods, exhibit noticeable uncertainties. Those uncertainties also present multiple hotspots which may be attributed to different impacts from different factors. By applying the IFA method, the results suggest the copula structure would likely be ranked as one of the top 2 impact factors for predictions of failure probabilities (FPs) in the scenarios of AND, and Kendall, with its contributions higher than 30% for FP in Kendall (more than 40% at Washington) and more than 25% for FP in Kendall (larger than 40% at Philadelphia). In comparison, the copula structure may not pose a visible effect on the predictive uncertainty for FP in OR, with its contribution possibly less than 5% under long‐term service time periods. However, the marginal distributions would have higher effects on FP in OR than the effects on the other two FPs. Particularly, the marginal distribution for the extreme variable with high skewness and kurtosis values tends to be ranked as one of the most significant impact factors for FP in OR. Also, the overall impacts from parametric uncertainties in both marginal and dependence models cannot be neglected for the predictions of all three FPs with their contributions probably larger than 20% under a short service time period. Compared with the traditional multilevel factorial analysis, the IFA method can provide more reliable characterization for uncertainty contributors in multi‐hazard risk analyses, since the traditional method seems to significantly overestimate the contributions from parameter uncertainties.

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Multivariate environmental contours using C-vine copulas

Cited by 62 publications

References 28 publications

Offshore infrastructure planning using a vine copula approach for environmental conditions: an application for replacement maintenance of tidal energy infrastructure

Offshore infrastructure planning using a vine copula approach for environmental conditions: an application for replacement maintenance of tidal energy infrastructure

Sales Comparison Approach Indicating Heterogeneity of Particular Type of Real Estate and Corresponding Valuation Accuracy

Tracing Uncertainty Contributors in the Multi‐Hazard Risk Analysis for Compound Extremes

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