Data-driven probabilistic post-earthquake fire ignition model for a community
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 revie...
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Published in | Fire safety journal Vol. 94; pp. 33 - 44 |
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Main Authors | , , |
Format | Journal Article Web Resource |
Language | English |
Published |
Lausanne
Elsevier Ltd
01.12.2017
Elsevier BV Elsevier Science |
Subjects | |
Online Access | Get full text |
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Summary: | 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.
•Fire Following Earthquake modules in Hazus and MAEViz/Ergo are investigated.•A probabilistic post-earthquake fire ignition model is proposed based on historical data.•The proposed model provides results at census tract and individual building levels.•The model and a GIS-based platform can be used to evaluate resiliency of a community. |
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Bibliography: | scopus-id:2-s2.0-85030659348 |
ISSN: | 0379-7112 1873-7226 1873-7226 |
DOI: | 10.1016/j.firesaf.2017.09.005 |