Predicting chemical parameters of the water from diatom abudance in lake Prespa and its tributaries

In this work, we are modelling the physic-chemical parameters of water using bioindicator data (diatom taxa abundance data). Chemical status of the water (or water quality class) is defined by the values of measured physic-chemical parameters. Traditional approach to model these data is to learn a separate model for each parameter and then derive a global overview with some kind of summarization over the multiple models. Another approach is to learn a single model that describes all parameters (multi target approach). We explore these approaches and apply them on data from Lake Prespa and its tributary rivers. The obtained models revealed interesting connections between the diatom taxa and the water quality (i.e. the values of the chemical parameters).