Predicting Chemical Parameters of River Water Quality from Bioindicator Data

We address the problem of inferring chemical parameters of river water quality from biological ones. This task is important for enabling selective chemical monitoring of river water quality. We apply machine learning, in particular regression tree induction, to biological and chemical data on the water quality of Slovenian rivers. Regression trees are constructed that predict values of chemical parameters from data on the presence of bioindicator taxa at the species and family levels.