Predicting chlorophyll-a in freshwater lakes by hybridising process-based models and genetic algorithms

This paper describes the application of several machine learning techniques to modify a process-based difference equation. The original process equation was developed to model phytoplankton abundance based on measured limnological and climate variables. A genetic algorithm is shown to be capable of calibrating the constants of the process model, based on the data describing a lake environment. The resulting process model has a significantly improved performance based on unseen test data. A symbolic genetic algorithm is then applied to the process model to evolve new expressions for the grazing term of the equation. The results indicate that this approach can be used to explore new process formulations and to improve the generalisation and predictive response of process models.