Title: Use of qualitative constraints in modelling of the Lake Glumso

Authors: Daniel Vladusic, Boris Kompare, Ivan Bratko

Addresses: Faculty of Computer and Information Science, Trzaska 25, 1000 Ljubljana, Slovenia. ' Faculty of Computer and Information Science, Trzaska 25, 1000 Ljubljana, Slovenia. ' Faculty of Computer and Information Science, Trzaska 25, 1000 Ljubljana, Slovenia

Abstract: This paper describes modelling of time behaviour of phytoplankton and zooplankton in the Danish lake Glumso with a recently developed approach to machine learning in numerical domains, called Q2 learning. An essential part of this approach is qualitative constraints which were either handcrafted using knowledge from the Lotka-Volterra predator-prey model or induced directly from the collected data with a program called QUIN. The induced models were evaluated by a domain expert. We performed a comparison between numerical results of the Q2 learning approach and standard machine learning algorithms. The results suggest that use of qualitative constraints leads to more accurate quantitative predictions.

Keywords: qualitative reasoning; machine learning; numerical prediction; Lake Glumso; modelling; time behaviour; phytoplankton; zooplankton; Denmark.

DOI: 10.1504/IJEP.2007.015668

International Journal of Environment and Pollution, 2007 Vol.31 No.1/2, pp.107 - 124

Published online: 07 Nov 2007 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article