@article{ref1,
title="Data mining on fire records of New South Wales, Sydney",
journal="Procedia engineering",
year="2014",
author="Lee, Eric Wai-ming and Yeoh, Guan-heng and Cook, Morgan and Lewis, Chris",
volume="71",
number="",
pages="328-332",
abstract="This study gathered fire records from the Fire and Rescue New South Wales (F&RNSW) for investigating the most relevant event to the fire accident. Support vector machine was adopted to mimic the correlation between the information of the building and occupants and the occurrence of fire accident. The percentage of correct prediction is 65% which is considered reasonable since noise is expected to be embedded in the data of the fire records. Bayesian approach was also adopted to analyze the relevancies of the binary input parameters to the fire occurrence. Monte Carlo simulation was conducted. The result shows that the Special-Risk-Building and Smokers are the two parameters most relevant to the occurrence of fire accident.<p />",
language="en",
issn="1877-7058",
doi="10.1016/j.proeng.2014.04.047",
url="http://dx.doi.org/10.1016/j.proeng.2014.04.047"
}