Binomial-Bivariate Log-Normal Compound Model and its Application on Probability Estimation of Extreme Sea State

Abstract

Extreme value analysis is an indispensable method to predict the probability of marine disasters and calculate the design conditions of marine engineering. The rationality of extreme value analysis can be easily affected by the lack of sample data. The peaks over threshold (POT) method and compound extreme value distribution (CEVD) theory are effective methods to expand samples, but they still rely on long-term sea state data. To construct a probabilistic model using short-term sea state data instead of the traditional annual maximum series (AMS), the binomial-bivariate log-normal CEVD (BBLCED) model is established in this thesis. The model not only considers the frequency of the extreme sea state, but it also reflects the correlation between different sea state elements (wave height and wave period) and reduces the requirement for the length of the data series. The model is applied to the calculation of design wave elements in a certain area of the Yellow Sea. The results indicate that the BBLCED model has good stability and fitting effect, which is close to the probability prediction results obtained from the long-term data, and reasonably reflects the probability distribution characteristics of the extreme sea state. The model can provide a reliable basis for coastal engineering design under the condition of a lack of marine data. Hence, it is suitable for extreme value prediction and calculation in the field of disaster prevention and reduction.

Keywords

Bivariate compound extreme value distribution / Double-threshold sampling / Extreme sea state / Short-term data / Probabilistic prediction