Predicting the category of fire department operations

Kevin Pirklbauer, Rainhard Dieter Findling

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

1 Zitat (Scopus)

Abstract

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.

OriginalspracheEnglisch
Titel21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019 - Proceedings
Redakteure/-innenMaria Indrawan-Santiago, Eric Pardede, Ivan Luiz Salvadori, Matthias Steinbauer, Ismail Khalil, Gabriele Anderst-Kotsis
Herausgeber (Verlag)Association for Computing Machinery
Seiten659-663
Seitenumfang5
ISBN (elektronisch)9781450371797
DOIs
PublikationsstatusVeröffentlicht - 2 Dez 2019
Veranstaltung21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019 - Munich, Deutschland
Dauer: 2 Dez 20194 Dez 2019

Publikationsreihe

NameACM International Conference Proceeding Series

Konferenz

Konferenz21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019
Land/GebietDeutschland
OrtMunich
Zeitraum02.12.201904.12.2019

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