Author(s)

Agaji Iorshase¹, Shangbum F. Caleb²

¹Department of Mathematics/Statistics/Computer Science, University of Agriculture, Makurdi, Nigeria.
²Department of Computer Science, Nigerian Army Institute of Technology & Environmental Studies, Makurdi, Nigeria.

Incessant fire-outbreak in urban settlements has remained intractable especially in developing countries like Nigeria. This is often characterized by grave socio-economic aftermath effects. Urban fire outbreak in Nigerian cities has been on increase in recent times. The major problem faced by fire fighters in Nigerian urban centres is that there are no mechanisms to detect fire outbreaks early enough to save lives and properties. They often rely on calls made by neighbours or occupants when an outbreak occurs and this accounts for the delay in fighting fire outbreaks. This work uses Artificial Neural Networks (ANN) with backpropagation method to detect the occurrence of urban fires. The method uses smoke density, room temperature and cooking gas concentration as inputs. The work was implemented using Java programming language and results showed that it detected the occurrence of urban fires with reasonable accuracy. The work is recommended for use to minimize the effect of urban fire outbreak.

Fire-Outbreak Detection, Neural Network, Urban Fires, Backpropagation, Sigmoid Transfer Function, Fire Alert, Temperature, Smoke Density, Cooking Gas Concentration, Weights

Iorshase, A. and Caleb, S. (2016) A Neural Based Experimental Fire-Outbreak Detection System for Urban Centres. Journal of Software Engineering and Applications, 9, 71-79. doi: 10.4236/jsea.2016.93006.