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Use of machine learning for determining phytoplankton dynamic on station RV001 in front of Rovinj (northern Adriatic) (CROSBI ID 196723)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Volf, Goran ; Kompare, Boris ; Ožanić, Nevenka Use of machine learning for determining phytoplankton dynamic on station RV001 in front of Rovinj (northern Adriatic) // Engineering review (Technical Faculty University of Rijeka), 34 (2014), 181-187

Podaci o odgovornosti

Volf, Goran ; Kompare, Boris ; Ožanić, Nevenka

engleski

Use of machine learning for determining phytoplankton dynamic on station RV001 in front of Rovinj (northern Adriatic)

The paper describes the use of machine learning for modeling phytoplankton on data from station RV001 in front of Rovinj which well represents the main processes in the open northern Adriatic (NA). NA is the shallowest, and also one of the most productive areas in the Adriatic Sea, as well as in the entire Mediterranean. In order to contribute to the understanding of phytoplankton dynamic in the NA, on data covering physical, biological and chemical parameters machine learning (ML) techniques were used. The final result is the construction of the models in the form of regression and model trees, respectively ; there were constructed models that explain the dynamics of phytoplankton concentrations on mentioned station as a result of independent environmental variables. Models in an affordable way combine and show knowledge collected by measurements during 35 year period, which contributes to a better understanding of the functioning of the NA ecosystem.

Northern Adriatic ; Machine learning ; Phytoplankton

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Podaci o izdanju

34

2014.

181-187

objavljeno

1330-9587

Povezanost rada

Građevinarstvo

Poveznice
Indeksiranost