Modelling and prediction of phytoplankton growth with equation discovery

Abstract

In contrast with traditional modeling methods, which are used to identify parameter values of a model with known structure, equation discovery systems identify the structure of the model also. The model generated with such systems can give experts a better insight into the measured data and can be also used for predicting future values of the measured variables. This paper presents lagramge, an equation discovery system that allows the user to define the space of possible model structures and to make use of domain specific expert knowledge in the form of function definitions. We use lagramge to automate the modeling of phytoplankton growth in lake Glumsoe, Denmark. The structure of the model constructed with lagramge agrees with human experts’ expectations. The model can be successfully used for long term prediction of phytoplankton concentration during algal blooms.