This inter-disciplinary study presents a novel mathematical simulation model based on an algorithm for the summarization of self-organizing maps ensembles applied under the case-based reasoning (CBR) methodology to perform forecasting tasks. This methodology represents a knowledge-extraction frame, where past information is used to generate new solutions to new problems. The novel summarization algorithm based on topology-preserving models organizes the stored information simplifying the retrieval of the most useful information from the case base. This algorithm is used to organize the case base and to improve the speed and efficiency of the retrieval phase of the CBR cycle within the explained predicting system. The developed mathematical system was applied to a real case of study: a forest fire forecasting data set. Forest fires represent an environmental risk that should be predicted in order to avoid further damages. This novel system was able to predict the future situation of geographic areas after a forest fire had been originated
