short-paper

Predicting the Category of Fire Department Operations

Authors:

Kevin Pirklbauer,

Rainhard Dieter FindlingAuthors Info & Claims

iiWAS2019: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services

Pages 659 - 663

https://doi.org/10.1145/3366030.3366113

Published: 22 February 2020 Publication History

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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Cited By

Sirri NGuyeux C(2025)Air Quality Impact on Firefighter Interventions: Factors AnalysisBig Data and Internet of Things10.1007/978-3-031-74491-4_30(389-403)Online publication date: 3-Jan-2025
https://doi.org/10.1007/978-3-031-74491-4_30
Mallouhy RGuyeux CJaoude CMakhoul A(2022)Predicting fire brigades' operations based on their type of interventions2022 International Wireless Communications and Mobile Computing (IWCMC)10.1109/IWCMC55113.2022.9825380(606-610)Online publication date: 30-May-2022
https://doi.org/10.1109/IWCMC55113.2022.9825380
Cerna SGuyeux CLaiymani D(2022)The usefulness of NLP techniques for predicting peaks in firefighter interventions due to rare eventsNeural Computing and Applications10.1007/s00521-022-06996-x34:12(10117-10132)Online publication date: 26-Feb-2022
https://doi.org/10.1007/s00521-022-06996-x
Show More Cited By

Index Terms

Predicting the Category of Fire Department Operations
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Ubiquitous and mobile computing
  2. Visualization

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iiWAS2019: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services

December 2019

709 pages

ISBN:9781450371797

DOI:10.1145/3366030

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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JKU: Johannes Kepler Universität Linz
@WAS: International Organization of Information Integration and Web-based Applications and Services

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 February 2020

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iiWAS2019

iiWAS2019: The 21st International Conference on Information Integration and Web-based Applications & Services

December 2 - 4, 2019

Munich, Germany

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Cited By

Sirri NGuyeux C(2025)Air Quality Impact on Firefighter Interventions: Factors AnalysisBig Data and Internet of Things10.1007/978-3-031-74491-4_30(389-403)Online publication date: 3-Jan-2025
https://doi.org/10.1007/978-3-031-74491-4_30
Mallouhy RGuyeux CJaoude CMakhoul A(2022)Predicting fire brigades' operations based on their type of interventions2022 International Wireless Communications and Mobile Computing (IWCMC)10.1109/IWCMC55113.2022.9825380(606-610)Online publication date: 30-May-2022
https://doi.org/10.1109/IWCMC55113.2022.9825380
Cerna SGuyeux CLaiymani D(2022)The usefulness of NLP techniques for predicting peaks in firefighter interventions due to rare eventsNeural Computing and Applications10.1007/s00521-022-06996-x34:12(10117-10132)Online publication date: 26-Feb-2022
https://doi.org/10.1007/s00521-022-06996-x
Guyeux CMakhoul ABahi J(2022)How to Build an Optimal and Operational Knowledge Base to Predict Firefighters’ InterventionsIntelligent Systems and Applications10.1007/978-3-031-16072-1_41(558-572)Online publication date: 31-Aug-2022
https://doi.org/10.1007/978-3-031-16072-1_41
Pirklbauer KFindling R(2021)Storm Operation Prediction: Modeling the Occurrence of Storm Operations for Fire Stations2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops51409.2021.9430944(123-128)Online publication date: 22-Mar-2021
https://doi.org/10.1109/PerComWorkshops51409.2021.9430944
Cerna SArcolezi HGuyeux CRoyer-Fey GChevallier C(2021)Machine learning-based forecasting of firemen ambulances’ turnaround time in hospitals, considering the COVID-19 impactApplied Soft Computing10.1016/j.asoc.2021.107561109:COnline publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.asoc.2021.107561

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