Predicting long-term population dynamics with bagging and boosting of process-based models
A novel methodology for learning ensembles of process-based models for long-term predictions We adapt the methods of bagging and boosting to the task of modeling dynamic systems We identify the design decisions for learning such ensembles We apply and evaluate the performance of the developed algorithms on modeling tasks of population dynamics in aquatic ecosystems Ensembles of PBMs have better predictive performance than a single process-based model.
population dynamics bagging boosting process-based modeling ensembles machine learning predictive modeling