Using an effective self-deconvolution scheme in combination with multivariate Gaidai reliability approach to forecast areal wave heights

Abstract

Problem Presented case study enhances environmental research on the impact of climate change by means of application of stet-of-the-art multimodal Gaidai risk and natural hazard assessment method to the in-situ measured wave-height dynamics. Existing reliability methods do not always cope easily with multivariate dynamic environmental systems with high-dimensionality. Method Multimodal Gaidai risk and natural hazard assessment technique is especially well-suited for environmental multimodal dynamic systems, that are either numerically simulated or physically observed over a representative time period, producing coherent jointly quasi-ergodic time series. Data Offshore ocean and sea waves are essential for operational safety and dependable operation/production process of offshore installations such as offshore wind turbines, oil and gas platforms. Key results State-of-the-art non-parametric deconvolution extrapolation-type scheme had been utilized to generate extreme areal wave-heights forecasts. Obtained forecasts had been verified versus parametric 4 parameter Weibull fit. Current study advocates generic, yet efficient reliability and risk assessment approach for failure (damage or natural hazard) risk evaluations for spatiotemporal multi-dimensional, nonlinear environmental multimodal dynamic wave-driven systems.