TITLE:
A Neural Based Experimental Fire-Outbreak Detection System for Urban Centres
AUTHORS:
Agaji Iorshase, Shangbum F. Caleb
KEYWORDS:
Fire-Outbreak Detection, Neural Network, Urban Fires, Backpropagation, Sigmoid Transfer Function, Fire Alert, Temperature, Smoke Density, Cooking Gas Concentration, Weights
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.9 No.3,
March
23,
2016
ABSTRACT: 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.