Application of automated model discovery from data and expert knowledge to a real-world domain: Lake Glumsّ

In this paper, we apply automated modelling method Lagramge to the task of modelling phytoplankton dynamics in Lake Glumsّ, Denmark. The approach is based on integrating expert knowledge in the process of automated model induction from measured data. It supports modelling of ecosystem dynamics with ordinary differential equations by following the mass conservation law. The data set used in this paper comprises 2 years daily measurements of data needed for phytoplankton modelling in lake. In order to have sufficient data set for training and testing the models, the entire data set was divided in two parts, each containing 1 year of daily measurements. The expert knowledge supplied to Lagramge consists of elementary models of the basic ecological processes related to the food web dynamics and rules for combining elementary into complex models of the whole system. By applying Lagramge on Lake Glumsّ we discovered a set of phytoplankton models that showed good fit on the training data set. The models were evaluated by simulating them on testing data set, which revealed good performance of the models.