Data Mining on Fire Records of New South Wales, Sydney

被引：2

作者：

Lee, Eric Wai-ming ^{[1
]}

Yeoh, Guan-heng ^{[2
]}

Cook, Morgan ^{[3
]}

Lewis, Chris ^{[3
]}

机构：

[1] City Univ Hong Kong, Dept Civil & Architectural Engn, Hong Kong, Hong Kong, Peoples R China

[2] Univ New S Wales, Sch Mech & Mfg Engn, Sydney, NSW 2052, Australia

[3] Tire & Rescue NSW, Greenacre, NSW 2190, Australia

来源：

2013 INTERNATIONAL CONFERENCE ON PERFORMANCE-BASED FIRE AND FIRE PROTECTION ENGINEERING (ICPFFPE 2013) | 2014年 / 71卷

关键词：

bayes an theorem; fire records; monte carlo simulation; support vector machine;

D O I：

10.1016/j.proeng.2014.04.047

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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. (C) 2014 Published by Elsevier Ltd.

引用

页码：328 / 332

页数：5

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