Predicting the category of fire department operations

Kevin Pirklbauer, Rainhard Dieter Findling

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

1 Sitaatiot (Scopus)
10 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
Otsikko21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019 - Proceedings
ToimittajatMaria Indrawan-Santiago, Eric Pardede, Ivan Luiz Salvadori, Matthias Steinbauer, Ismail Khalil, Gabriele Anderst-Kotsis
KustantajaACM
Sivumäärä5
ISBN (elektroninen)9781450371797
DOI - pysyväislinkit
TilaJulkaistu - 2 joulukuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Information Integration and Web-Based Applications and Services - Munich, Saksa
Kesto: 2 joulukuuta 20194 joulukuuta 2019
Konferenssinumero: 21

Conference

ConferenceInternational Conference on Information Integration and Web-Based Applications and Services
LyhennettäiiWAS
Maa/AlueSaksa
KaupunkiMunich
Ajanjakso02/12/201904/12/2019

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