Simultaneous Prediction of Multiple Chemical Parameters of River Water Quality with TILDE

Environmental studies form an increasingly popular application domain for machine learning and data mining techniques. In this paper we consider two applications of decision tree learning in the domain of river water quality: a) the simultaneous prediction of multiple physico-chemical properties of the water from its biological properties using a single decision tree (as opposed to learning a different tree for each different property) and b) the prediction of past physico-chemical properties of the river water from its current biological properties. We discuss some experimental results that we believe are interesting both to the application domain experts and to the machine learning community.