Evolutionary Intelligent System for input parameter optimisation in environmental modelling: A case study in forest fire forecasting

The need for input parameter optimisation in environmental modelling is a long-known and very time-consuming task. However, to avoid tragedy, disaster propagation predictions have to satisfy hard real-time constraints. Especially small disaster control centres with limited computing resources require fast and efficient calibration methods to deliver reliable predictions in time. The combination of a clustering method together with a Genetic Algorithm is used as parameter optimisation technique in forest fire spread prediction. We formalise and demonstrate the potential of the resulting Evolutionary Intelligent System´s architecture to solve the complex problem of input parameter calibration on restricted simulation conditions.