Identifying a land use change cellular automaton by Bayesian data assimilation
We identify model structure and parameters of a land use change cellular automaton. Expert knowledge and empirical data are combined in Bayesian data assimilation. The calibrated model can reproduce observed land use patterns fairly good. Our output uncertainty comprises errors from model structure and calibration data.
land use change data assimilation model structure calibration cellular automata particle filter