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In this paper we present models of relationship b etween the diatoms community diversity indices (DIs) and the physico-chemical parameters u sing machine learning techniques. By taking relativ e abundances into account, a diversity index depends not only on species richness but also on the evenne ss, or equitability, with which individuals are distrib uted among the different time and space. Diversity indices provide important information about rarity and commonness of species in a community. Because the physical-chemical conditions of the environment al influence on the several diversity indices of th e diatoms community at once, it is more reliably to m del all the diversity indices together. For modelling of the DIs models we use the raw; as measured, values of the concentrations for the physical-chemical parameters and the diversity indi ces of the diatoms abundance. The well known machine learning techniques are used to express thi relationship: regression trees (RTs) and multi-ta rget regression trees (MTRT’s). The MTRT are more genera l than the RT, which predictive target is only one variable. The diversity indices are calculated for all diatoms of one measurement for 16 months, month ly and then are placed with the given physico-chemical parameters in one table. The results from the mode l have captured the ecological information with corre lation between 0.9 and 0.92 for unseen (test) data. Diversity indices have proved to be a reliable indi cator for the influence of the environment on the diatoms community. Temperature and conductivity com p nents together with the Zn concentration are most influenced factors on the diatoms biodiversity . This could lead to more widely research broad vie w in this direction of ecological modelling.

MULTI-TARGET MODELLING OF THE DIATOM DIVERSITY INDICES IN LAKE PRESPA