Probabilistic models for the prediction of a ship performance in dynamic ice

We introduce two probabilistic, data-driven models that predict a ship's speed and the situations where a ship is probable to get stuck in ice based on the joint effect of ice features such as the thickness and concentration of level ice, ice ridges, rafted ice, moreover ice compression is considered. To develop the models to datasets were utilized. First, the data from the Automatic Identification System about the performance of a selected ship was used. Second, a numerical ice model HELMI, developed in the Finnish Meteorological Institute, provided information about the ice field. The relations between the ice conditions and ship movements were established using Bayesian learning algorithms. The case study presented in this paper considers a single and unassisted trip of an ice-strengthened bulk carrier between two Finnish ports in the presence of challenging ice conditions, which varied in time and space. The obtained results show good prediction power of the models. This means, on average 80% for predicting the ship's speed within specified bins, and above 90% for predicting cases where a ship may get stuck in ice. We expect this new approach to facilitate the safe and effective route selection problem for ice-covered waters where the ship performance is reflected in the objective function.

Digital terrain model (DTM) of the central Fram Strait

Based on data from R/V Polarstern multibeam sonar surveys between 1984 and 1997 a high resolution bathymetry has been generated for the central Fram Strait. The area ensonified covers approx. 36,500 sqkm between 78°N-80°N and 0°E-7.5°E. Basic outcome of the investigation is a Digital Terrain Model (DTM) with 100 m grid spacing which was utilized for contouring and generation of a new series of bathymetric charts (AWI BCFS).