Abstract
The need for input parameter optimisation in environmental modelling is long known. Real-time constraints of disaster propagation predictions require fast and efficient calibration methods to deliver reliable predictions in time to avoid tragedy. Lately, evolutionary optimisation methods have become popular to solve the input parameter problem of environmental models. Applying a knowledge-guided Genetic Algorithm (GA) we demonstrate how to speed up parameter optimsation and consequently the propagation prediction of environmental disasters. Knowledge, obtained from historical and synthetical disasters, is stored in a knowledge base and provided to the GA in terms of a knowledge chromosome. Despite of increased loads of knowledge, its retrieval times can be kept near-constant. During GA mutation, ranges of selected parameters are limited forcing the GA to explore promising solution areas. Experiments in forest fire spread prediction show how time-consuming fitness evaluations of the GA could be lowered remarkably to cope with real-time capabilities maintaining the error magnitude.
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