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We present an approach for evaluation of a heat release rate of compartment fires. The approach is based on the idea of matching the actual condition of the fire to the pre- generated CFD simulations. We use an IR image of imprint of the temperature on the ceiling as a similarity relationship between actual fire and the set of the simulations. We extract the invariants, features and similarity measures of the fires using machine learning approach. Index Terms—Inverse Fire Modelling, Artificial Intelligence, Classification, Image Processing, Fire Services

Evaluation of a Heat Release Rate based on Massively Generated Simulations and Machine Learning Approach