@article{ref1,
title="Data-driven probabilistic post-earthquake fire ignition model for a community",
journal="Fire safety journal",
year="2017",
author="Elhami Khorasani, Negar and Gernay, Thomas and Garlock, Maria",
volume="94",
number="",
pages="33-44",
abstract="Fire following earthquake (FFE), a cascading multi-hazard event, can cause major social and economical losses in a community. In this paper, two existing post-earthquake fire ignition models that are implemented in Geographic Information System (GIS) based platforms, Hazus and MAEViz/Ergo, are reviewed. The two platforms and their FFE modules have been studied for suitability in community resiliency evaluations. Based on the shortcomings in the existing literature, a new post-earthquake fire ignition model is proposed using historical FFE data and a probabilistic formulation. The procedure to create the database for the model using GIS-based tools is explained. The proposed model provides the probability of ignition at both census tract scale and individual buildings, and can be used to identify areas of a community with high risk of fire ignitions after an earthquake. The model also provides a breakdown of ignitions in different building types. Finally, the model is implemented in MAEViz/Ergo to demonstrate its application in a GIS-based software.<p /> <p>Language: en</p>",
language="en",
issn="0379-7112",
doi="10.1016/j.firesaf.2017.09.005",
url="http://dx.doi.org/10.1016/j.firesaf.2017.09.005"
}