Storm Operation Prediction: Modeling the Occurrence of Storm Operations for Fire Stations

Kevin Pirklbauer, Rainhard Dieter Findling

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

Abstract

Publicly available fire operation data opens new ways to assist fire stations in planning and resource distribution tasks. Previous work has predicted attributes of certain operations, as well as of thunderstorms, but fire department storm operations themselves have not been predicted yet. We present an approach to predict if storm operations will occur for individual fire stations on specific days, based on time, location, and weather data. As days with storm operations are rare, we artificially balance samples with SMOTE, then compare the prediction performance of different machine learning models with an outlier detection on unbalanced data and uninformed prediction models as baseline. To evaluate our approach, we aggregate datasets for 10 fire stations in Upper Austria, Austria, and predict their storm operations. Our approach thereby achieves a median AUC of 0.91 across fire stations, which is an improvement of 0.44 over baseline models.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2021
PublisherIEEE
Pages123-128
Number of pages6
ISBN (Electronic)9781665404242
DOIs
Publication statusPublished - 25 May 2021
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Pervasive Computing and Communications Workshops - Kassel, Germany
Duration: 22 Mar 202126 Mar 2021
https://www.percom.org/

Publication series

NameIEEE international conference on pervasive computing and communications workshops
PublisherIEEE

Conference

ConferenceIEEE International Conference on Pervasive Computing and Communications Workshops
Abbreviated titlePerCom Workshops
Country/TerritoryGermany
CityKassel
Period22/03/202126/03/2021
Internet address

Keywords

  • predictive models machine learning

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