Theory of Self-Reproducing Automata
Theory of Self-Reproducing Automata
Modeling of the 2001 lava flow at Etna volcano by a Cellular Automata approach
Environmental Modelling & Software
Environmental Modelling & Software
Macroscopic cellular automata for groundwater modelling: A first approach
Environmental Modelling & Software
Environmental Modelling & Software
Parameter identification of the STICS crop model, using an accelerated formal MCMC approach
Environmental Modelling & Software
Identifying a land use change cellular automaton by Bayesian data assimilation
Environmental Modelling & Software
Hi-index | 0.00 |
Modelling an environmental process involves creating a model structure and parameterising the model with appropriate values to accurately represent the process. Determining accurate parameter values for environmental systems can be challenging. Existing methods for parameter estimation typically make assumptions regarding the form of the Likelihood, and will often ignore any uncertainty around estimated values. This can be problematic, however, particularly in complex problems where Likelihoods may be intractable. In this paper we demonstrate an Approximate Bayesian Computational method for the estimation of parameters of a stochastic CA. We use as an example a CA constructed to simulate a range expansion such as might occur after a biological invasion, making parameter estimates using only count data such as could be gathered from field observations. We demonstrate ABC is a highly useful method for parameter estimation, with accurate estimates of parameters that are important for the management of invasive species such as the intrinsic rate of increase and the point in a landscape where a species has invaded. We also show that the method is capable of estimating the probability of long distance dispersal, a characteristic of biological invasions that is very influential in determining spread rates but has until now proved difficult to estimate accurately.