Comparison of Linear and Rank-Correlation for the Sea State Estimation

To protect the marine environment and increase the safety of ships, knowledge of the state of the sea is crucial. In this study, sea state estimation was performed using an algorithm for unimodal and short-crested sea states, varying the linear and rank correlation coefficients accordingly. More specifically, Pear-son, Spearman and Kendall correlation coefficients are employed and compared to detect the best method in terms of accuracy and computational time. The results are encouraging when Spearman is used while Pearson provides the worst reconstruction in terms of accuracy, whereas Kendall causes too high computational costs, especially if the number of input time series is increased.