Lagergren, John H

ISU ReD: Research and eData

Hybrid Modeling for Forecasting Population DymanicsJohn Lagergren, Amanda Reeder, Franz Hamilton, Ralph Smith, Kevin FloresAbstractWe present a novel hybrid approach for forecasting dynamical systems and demonstrate our method-ology on population data from cannibalistic flour beetles. In our proposed method, a portion of amechanistic model’s equations are used to predict a subset of the variables while a model-free predic-tion method is used to predict the remaining. The direct benefit of this is a reduction in complexityof the required parameter estimation problem which leads to an overall increase in prediction accuracyof future population behavior. Utilizing a Bayesian parameter estimation framework, we implementeduncertainty quantification analysis and found that the increase in prediction accuracy, afforded by ourhybrid approach, is associated with lower levels of parameter correlations and Fisher Information Matrixrank deficiency. Though demonstrated on beetle population dynamics, our approach can be generalizedto forecasting any ecological system in which multivariate time series data are available.Keywords: dynamical systems, prediction, parameter estimation, population dynamics, uncertainty quan-tification.1

Hybrid Modeling for Forecasting Population Dynamics

http://ir.library.illinoisstate.edu/cgi/viewcontent.cgi?article=1064&context=beer

Hybrid Modeling for Forecasting Population Dynamics

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ISU ReD: Research and eData