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This study presents a state-of-the-art risk evaluation approach, designed for spatiotemporal multivariate environmental dynamic wind-wave systems, being either measured or Monte Carlo (MC) numerically simulated over a representative time-lapse. The main objective of this study has been to benchmark/validate the effectiveness and accuracy of the Gaidai multivariate risk evaluation methodology, using the application to in situ raw windspeeds, along with correlated wave-heights measurements, delivered by North Pacific area of the National Oceanic and Atmospheric Administration (NOAA) ocean buoys. The current study outlines a novel risk evaluation methodology, suitable for environmental dynamic systems, that are either MC numerically modeled, or directly physically measured. The intercorrelations between the wind-wave environmental system’s critical/key dimensions and components, along with the high dimensionality of complex environmental systems, are not easily addressed by contemporary classic reliability methods. The objective of this study is to apply a novel reliability/risk evaluation methodology to a combined windspeed and correlated wave-height raw data set, recorded by the NOAA buoys within the North Pacific area, to demonstrate the efficiency of the proposed methodology. By reliability/risk assessment in the current study authors primarily mean probability forecast of certain multivariate hazard event. It is well known that when combined, windspeeds along with correlated wave heights form nonlinear dynamic environmental systems, that are complex, multidimensional, nonstationary, and yet intercorrelated. Global warming is only one of several significant factors that have ongoing impact on ocean windspeeds along with correlated wave heights, and environmental system risk evaluation is essential for marine, naval, and offshore structures, operating within specific in situ offshore areas of interest subject to realistic in situ ocean/sea weather conditions. The main goal of this study had been to benchmark and validate novel multivariate risk analysis methodology, making it possible to extract essential information directly from in situ raw environmental measurements. The methodology presented in this study opens the possibility of efficiently yet accurately assessing global failure/damage and hazard risks for multivariate nonstationary nonlinear environmental sea/ocean wind-wave systems.
Gaidai et al. (Fri,) studied this question.