engleski

An application of Self-Organising Maps method in recent Adriatic environmental studies and its perspectives

We present three recent research studies performed in the Adriatic Sea which applied the Self-Organising Maps (SOM) method, a kind of neural network, to various environmental data. The first study applied the SOM method to a long (50 yrs) series of thermohaline, dissolved oxygen and nutrient data measured over a deep (1200 m) Southern Adriatic Pit, in order to extract characteristic deep water mass patterns and their variability. The second study encompassed the classification of surface current patterns measured by HF radars over the northernmost part of the Adriatic, by applying the SOM method to the HF radar data and operational mesoscale meteorological model surface wind fields. The major output from this study was a resemblance between characteristic ocean current distribution patterns with and without wind data introduced to the SOM, implying the dominant wind driven dynamics over a local scale. That nominates the SOM method feasible and a basis for generation of very fast real-time forecast models over limited domains, based just on the existing atmospheric forecasts and basin-oriented ocean experiments. The third study is attempting to find the connection between underwater sound intensity and the distribution of bottlenose dolphins over their natural habitat in coastal eastern Adriatic waters. Altogether, a usefulness of the SOM method has been recognized in different aspects of basin-scale ocean environmental studies, and may be a useful tool in future investigations of understanding of the multi-disciplinary dynamics over a basin, including the creation of operational environmental forecasting systems.

neural networks; oceanography

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