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On the Use of Machine Learning Models to Forecast Flashover Occurrence in a Compartment



Jun Wang, Wai Cheong Tam, Paul A. Reneke, Richard D. Peacock, Thomas G. Cleary, Eugene Yujun Fu, Grace Ngai, Hong va Leong


This paper presents a study to examine the potential use of machine learning algorithms to build a model to forecast the likelihood of flashover occurrence for a single-floor multi-room compartment. Synthetic temperature data for heat detectors from different rooms are generated using Fire Data Generator. More than 1000 simulation cases are considered and a total of 8 million data points are utilized for model development. The development of P-Flash (Prediction model for Flashover occurrence) is presented. Two special treatments: sequence segmentation and learning from fitting, are proposed to overcome the temperature limitation of heat detectors in real-life fire scenarios and to enhance prediction capabilities to forecast the future likelihood of flashover occurrence even with situations where there is no temperature data from all detectors. Experimental evaluation shows that P-Flash offers reliable prediction. The model performance is approximately 83.2 % and 81.7 %, respectively, for current and future likelihood of flashover occurrence, considering heat detector failure at 150 ̊C. Results demonstrate that P-Flash, a new data-driven model, is feasible to provide fire fighters real-time, trustworthy, and actionable information to enhance situation awareness, operational effectiveness, and safety for firefighting.
Proceedings Title
17th International Conference on Automatic Fire Detection (AUBE 20) & Suppression, Detection and
Signaling Research and Applications Conference (SUPDET 2020)
Conference Dates
September 15-17, 2020
Conference Location
Mülheim an der Ruhr, -1


Machine learning, flashover prediction, synthetic data, upper gas layer temperature, heat detectors
Created September 15, 2020, Updated June 13, 2020