Predicting the category of fire department operations

Kevin Pirklbauer, Rainhard Dieter Findling

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)
34 Downloads (Pure)


Voluntary fire departments have limited human and material resources. Machine learning aided prediction of fire department operation details can benefit their resource planning and distribution. While there is previous work on predicting certain aspects of operations within a given operation category, operation categories themselves have not been predicted yet. In this paper we propose an approach to fire department operation category prediction based on location, time, and weather information, and compare the performance of multiple machine learning models with cross validation. To evaluate our approach, we use two years of fire department data from Upper Austria, featuring 16.827 individual operations, and predict its major three operation categories. Preliminary results indicate a prediction accuracy of 61%. While this performance is already noticeably better than uninformed prediction (34% accuracy), we intend to further reduce the prediction error utilizing more sophisticated features and models.

Original languageEnglish
Title of host publication21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019 - Proceedings
EditorsMaria Indrawan-Santiago, Eric Pardede, Ivan Luiz Salvadori, Matthias Steinbauer, Ismail Khalil, Gabriele Anderst-Kotsis
Number of pages5
ISBN (Electronic)9781450371797
Publication statusPublished - 2 Dec 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Information Integration and Web-Based Applications and Services - Munich, Germany
Duration: 2 Dec 20194 Dec 2019
Conference number: 21


ConferenceInternational Conference on Information Integration and Web-Based Applications and Services
Abbreviated titleiiWAS


  • Fire department operation prediction
  • Machine learning
  • Operation category prediction


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