Image Classification by Deep Neural Network of Event-Type Anomalies in The Southwestern Baltic Sea

Abstract

In the paper we propose a binary classification method to identify episodes of anomalies in physicochemical parameters related to mixing and exchange of water masses. For training and validation of classifier we use high resolution time series from the Boknis Eck monitoring station in the southwestern Baltic Sea. To study the role of air ocean coupling, in addition to ocean parameters, we use high resolution wind speed observations from the Kiel lighthouse weather station. The detection accuracy of anomalies relies upon training of deep neural network on image data generated from historical data interval of ocean parameters. Our data driven analysis suggests strong causality between variations in atmospheric wind and ocean physiochemistry that underlies short term ocean exchange processes in the study area.