simulation

Data mining techniques for analysing complex simulation models

Individual-based models of plant populations and communities can be highly complex, reflecting the underlying dynamics of the natural systems under study. Analysing and understanding model behaviour can be extremely challenging. To address this we are collaborating with researchers from the Department of Knowledge Technologies of the Jožef Stefan Institute, Slovenia on the application of data mining and machine learning techniques to the analysis of the IBMs we have developed.

Model analysis

Machine learning methods are being used to analyse the relationship between simulation outputs with IBM inputs (model parameters) in order to gain insight into the behaviour of the model system. Here we rely on a Monte Carlo approach in which simulations are based on IBM parameter values sampled at random from across a predefined parameter space. By applying machine learning methods, we can generalise over the specific simulations made and derive more general rules concerning the behaviour of the system.

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