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Use of qualitative constraints in modelling of the Lake Glumso

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

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