Use of artificial neural networks to identify the origin of green macroalgae

This study demonstrates application of artificial neural networks (ANNs) for identifying the origin of green macroalgae (Enteromorpha sp. and Cladophora sp.) according to their concentrations of Cd, Cu, Ni, Zn, Mn, Pb, Na, Ca, K and Mg. Earlier studies confirmed that algae can be used for biomonitoring surveys of metal contaminants in coastal areas of the Southern Baltic. The same data sets were classified with the use of different structures of radial basis function (RBF) and multilayer perceptron (MLP) networks. The selected networks were able to classify the samples according to their geographical origin, i.e. Southern Baltic, Gulf of Gdańsk and Vistula Lagoon. Additionally in the case of macroalgae from the Gulf of Gdańsk, the networks enabled the discrimination of samples according to areas of contrasting levels of pollution. Hence this study shows that artificial neural networks can be a valuable tool in biomonitoring studies.

► ANNs are effective tools for identifying the geographical origin of macroalgae. ► Chemical element contents are suitable descriptors to differentiate among the areas. ► MLP and RBF are useful in assessment of the quality of the study area. ► Recognition of algae samples affected by contaminants is possible.