Data-driven probabilistic post-earthquake fire ignition model for a community

Negar Elhami Khorasani, Thomas Gernay, Maria Garlock

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)33-44
Number of pages12
JournalFire Safety Journal
StatePublished - Dec 2017

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • General Materials Science
  • Safety, Risk, Reliability and Quality
  • General Physics and Astronomy


  • Community
  • Fire following earthquake
  • Hazus
  • Ignition
  • MAEViz/Ergo
  • Probabilistic


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