MATHEMATICAL MODELS FOR DECISION SUPPORT IN WILDFIRES PREVENTION WITH INCOMPLETE INITIAL DATA
Annotation
The basic problems of effective measures selection in forest fires prevention are discussed. Mathematical models are developed for quantitative estimating the fire hazard level and possible damage in the case when initial information is incomplete. The presented models treat wildfire as a random event; Walley criterion or the maximality condition is applied for decision support in forest fire prevention measures determining. The generalized Dirichlet model and Bayesian approach are used for assessment of forest fire hazard level. The proposed models are easy to implement in software and employed in complex systems of decision support.
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