Suchergebnisse

Abstract. Impact database development and application for risk analysis and management promote the usage of self-learning computer systems with elements of artificial intelligence. Such system learning could be successful when the databases store the complete information about each
event, parameters of the simulation models, the range of its application, and residual errors. Each new description included in the database could
increase the reliability of the results obtained with application of
simulation models. The calibration of mathematical models is the first step
to self-learning of automated systems. The article describes the events'
database structure and examples of calibrated computer models as applied to the impact of expected emergencies and risk indicator assessment. Examples of database statistics usage in order to rank the subjects of the Russian Federation by the frequency of emergencies of different character as well as risk indicators are given.