A feed-forward back-propagation neural network (NN) is proposed to model number of firefighters responding to different fire incidents. Such a predictor model can estimate number of firefighter personnel required to tackle new incidents. This a priori information at the time of dispatch can help saving unnecessary efforts in low-risk incidents while focussing on high-risk ones to reduce overall damages and injuries caused by the fire incidents.
A fully connected multilayer NN was adapted as the prediction model. The network was trained on a large number of fire incident records reported in Toronto area between 2000 and 2006 and then its performance was evaluated on another set of never seen records. Two types of prediction were done to model number of responding personnel: a rough category prediction and an exact number prediction.
Results obtained reported a very promising ability of this approach to model number of firefighters responding to a fire incident.
Such a model can significantly reduce uncertainties on the requirements needed for tackling a fire incident once it is reported.
The study has been founded by GEOIDE (www.geoide.ulaval.ca) as project number 99, phase III. The authors would like to thank the Ontario Office of the Fire Marshall for providing raw data.
Sadeghi-Naini, A. and Asgary, A. (2013), "Modeling number of firefighters responding to an incident using artificial neural networks", International Journal of Emergency Services, Vol. 2 No. 2, pp. 104-118. https://doi.org/10.1108/IJES-03-2012-0001
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