doi:10.3850/978-981-08-7619-7_P046

ABSTRACT

The focus of this paper are two new methods for predicting the extreme values of a random wave field in both space and time. Both methods rely on the use of time series expressing spatial extremes. These time series are constructed by sampling the available realizations of the random field over a suitable grid defining the domains in question and extracting the extreme values for each time point. In this way, time series of spatial extremes are produced. The realizations of the random field are obtained from either measurements or Monte Carlo simulations. The obtained time series provide the basis for estimating the extreme value distribution using recently developed techniques for time series, which results in an accurate practical procedure. The proposed prediction methods are applied to the specific case of a second-order random wave field.

Keywords: Extreme value distribution, Non-Gaussian random field, Second-order wave field, Monte Carlo simulations, Air gap problem.

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