Abstract Simulation systems that predict the propagation of environmental emergencies have to satisfy hard real-time constraints to prevent tragedy. To obtain more reliable forecasts, input parameter calibration mechanisms should be integrated because it is often impossible to measure highly dynamic input parameters in a correct and timely manner. Evolutionary optimisation methods showed promising calibration potential but involve numerous expensive fitness evaluations. This makes their application to time-restricted real-world problems impractical, especially when only limited computing resources are available. We therefore propose a framework for efficient parameter calibration based on evolutionary intelligent systems. A genetic algorithm is joined with a case-based reasoner to form a hybrid calibration approach. The suggested framework allows the user to select the configuration of the calibration process according to emergency and prediction characteristics and available computing resources. The possibility to generate quick calibration estimates thus minimising the additional computational effort caused by the introduction of parameter calibration is highlighted. The framework was tested in the area of forest fire spread prediction. A case base was generated from real historical and synthetical forest fires. Experiments show that case-based reasoning generates results comparable to pure evolutionary optimisation approaches, clearly outperforming the latter in runtime.