Predicting the category of fire department operations

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A4 Artikkeli konferenssijulkaisussa
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Date

2019-12-02

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Department of Communications and Networking

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Language

en

Pages

5

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21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019 - Proceedings

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.

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Keywords

Fire department operation prediction, Machine learning, Operation category prediction

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DOI

10.1145/3366030.3366113

Citation

Pirklbauer, K & Findling, R D 2019, Predicting the category of fire department operations. in M Indrawan-Santiago, E Pardede, I L Salvadori, M Steinbauer, I Khalil & G Anderst-Kotsis (eds), 21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019 - Proceedings. ACM, International Conference on Information Integration and Web-Based Applications and Services, Munich, Germany, 02/12/2019. https://doi.org/10.1145/3366030.3366113

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https://urn.fi/URN:NBN:fi:aalto-202109299361

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[article-cris] Sähkötekniikan korkeakoulu / ELEC