Abstract The capability to predict changes of spatial regions is important for an intelligent system that interacts with the physical world. For example, in a disaster management scenario, predicting potentially endangered areas and inferring safe zones is essential for planning evacuations and countermeasures. Existing approaches usually predict such spatial changes by simulating the physical world based on specific models. Thus, these simulation-based methods will not be able to provide reliable predictions when the scenario is not similar to any of the models in use or when the input parameters are incomplete. In this paper, we present a prediction approach that overcomes the aforementioned problem by using a more general model and by analysing the trend of the spatial changes. The method is also flexible to adopt to new observations and to adapt its prediction to new situations.