Modeling the risk of structural fire incidents using a self-organizing map

作者: Ali Asgary , Ali Sadeghi Naini , Jason Levy

DOI: 10.1016/J.FIRESAF.2011.12.007

关键词: Quality (business)Fire safetyOperations researchSample (statistics)Fire riskMachine learningEngineeringRisk levelFire responseSelf-organizing mapArtificial intelligence

摘要: Abstract A Self-Organizing Map (SOM) is used to classify and assess the risk levels of structural fire incidents. Such an assessment can be not only for improving safety protection existing future structures, but also enhancing emergency responses This reduce damages injuries resulting from fires. The map has a 2D hexagonal lattice structure was applied on sample incident records Toronto which were reported between 2000 2006. Assessment results suggest that SOM approach able successfully incidents with different properties into their predefined level classes. In summary, proposed shows superior performance predicting risk, although quality quantity training samples critical success predictions

参考文章(29)
Traci J. Hess, Anna Lazarova McNab, Joseph S. Valacich, DESIGNING EMERGENCY RESPONSE APPLICATIONS FOR BETTER PERFORMANCE international conference on information systems. pp. 3- ,(2009)
Susan L. Rose-Pehrsson, Sean J. Hart, Thomas T. Street, Frederick W. Williams, Mark H. Hammond, Daniel T. Gottuk, Mark T. Wright, Jennifer T. Wong, Early Warning Fire Detection System Using a Probabilistic Neural Network Fire Technology. ,vol. 39, pp. 147- 171 ,(2003) , 10.1023/A:1024260130050
Yoshiaki Okayama, A primitive study of a fire detection method controlled by artificial neural net Fire Safety Journal. ,vol. 17, pp. 535- 553 ,(1991) , 10.1016/0379-7112(91)90052-Z
Kurt Hornik, Approximation capabilities of multilayer feedforward networks Neural Networks. ,vol. 4, pp. 251- 257 ,(1991) , 10.1016/0893-6080(91)90009-T
T. Kohonen, The self-organizing map Proceedings of the IEEE. ,vol. 78, pp. 1464- 1480 ,(1990) , 10.1109/5.58325
James C. Bezdek, A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms IEEE Transactions on Pattern Analysis and Machine Intelligence. ,vol. 2, pp. 1- 8 ,(1980) , 10.1109/TPAMI.1980.4766964