Measuring the Effect of Stochastic Perturbation Component in Cellular Automata Urban Growth Model

Urban environments are complex dynamic systems whose prediction of the future states cannot exclusively rely on deterministic rules. Although several studies on urban growth were carried out using different modelling approaches, the measurement of uncertainties was commonly neglected in these studies. This paper investigates the effect of uncertainty in urban growth models by introducing a stochastic perturbation method. A cellular automaton is used to simulate predicted urban growth. The effect of stochastic perturbation is addressed by comparing series of urban growth simulations based on different degree of stochastic perturbation randomness with the original urban growth simulation, obtained with the sole cellular automata neighbouring effects. These simulations are evaluated using cell-to-cell location agreement and a number of spatial metrics. The model framework has been applied to the Ourthe river basin in Belgium. The results show that the accuracy of the model is increased by introducing a stochastic perturbation component with a limited degree of randomness, in the cellular automata urban growth model.