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