peer reviewed ; Urban expansion models are widely used to understand, analyze and predict any peculiar scenario based on input probabilities. Modelling and uncer-tainty are concomitant, and can occur due to reasons ranging from–discrepancies in input variables, unpredictable model parameters, spatio-temporal variability between observations, or malfunction in linking model variables under two different spatio-temporal scenarios. However, uncertainties often occur because of the interplay of model elements, structures, and the quality of data sources employed; as input param-eters influence the behavior of cellular automaton (CA) models. Our study aims to address these uncertainties. While most studies consider neighborhood effects, timestep and spatial resolution, our study uniquely focuses on the susceptibility of multi density classes and varying cell size on uncertainty. Hence this chapter offers a theoretical elucidation of the concepts, sources, and strategies for managing uncer-tainty under various criteria as well as an algorithm for enumerating the model’s accuracy for Wallonia, Belgium.