Predictive technologies for strategic house fire management

Andrew Edwards, Stephen Smith, Peter Busch, Donald Winchester

Research output: Contribution to conferenceConference paperpeer-review


House fires have posed a threat to life and property in every society for millennia such that laws, organisations and work systems have been established to protect communities. Motivated by statistics published annually by the Australian Productivity Commission (2021) showing little change in the fatalities, injuries or costs associated with house fires, this research demonstrates that large repositories of publicly available information about house fire incidents can be used to create predictive decision tools that could lower the impact of house fires on society. Interpreted through an activity theory lens, this research demonstrates how data mining can identify common features in public datasets and be used to create predictive models to identify future instances of house fires. The research proposes that this information be used by government, firefighting organisations, insurers, not for profits and the public to better prepare when and where house fires are more likely to occur.
Original languageEnglish
Number of pages12
Publication statusPublished - 5 Dec 2022
EventAustralian Conference on Information Systems (ACSIS) 2022 - University of Melbourne, Melbourne, Australia
Duration: 4 Dec 20227 Dec 2022


ConferenceAustralian Conference on Information Systems (ACSIS) 2022
Abbreviated titleACIS 2022
Internet address


  • Activity Theory (CHAT)
  • Machine Learning
  • House Fires
  • Risk Management
  • Prediction


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