Modelling ocean parameters through graphical models

Ocean parameter modelling is an important task for many fields. While using simulations and simple statistical models may not yield desired results in reasonable time, using graphical models like Bayesian networks can address this problem. In this paper, we show the application of Bayesian networks to the tasks of estimating and predicting significant wave heights in the North Sea. Additionally we present the K2 IO algorithm, a modification to the K2 algorithm developed for the prediction task. Experiments show the possibilities and problems of estimation and prediction with Bayesian networks. They also show that the K2 IO algorithm produces a structure that is suitable for prediction in a shorter time than the K2 algorithm.