Using artificial intelligence algorithms in the investigation of mixed layer depth seasonal changes in the Barents Sea

The study aims at discovering features of seasonal changes in the mixed layer depth (MLD) of the Barents Sea in 1993–2020. Charts of the distribution of the MLD in the Barents Sea in 1993–2020 provided by the Copernicus Marine Service were used as the material of the study. Methods of the study : cluster analysis, machine learning, neuronal networks, the nearest neighbor method (kNN). Results. Classification of the data sets of the MLD distribution according to their seasonal features was carried out based on the modelling using AI algorithms and machine learning. It was concluded that winter is specified by two classes (increased/decreased values of the layer thickness). The third class includes spring and autumn when distributions of the MLD are close to one another, and the fourth class comprises summer (June-September) when the MLD grows very slowly. Practical relevance . The results will contribute to a better understanding of the hydrophysical processes of the Barents Sea and can further be used as series of independent variables to study the Barents Sea ecosystem and to estimate a stock and a catch forecast of commercial aquatic organisms.