Optimization of Firefighter Response with Predictive Analytics: Practical Application to Lisbon, Portugal

Research output: ThesisMaster's Thesis

Abstract

Time is a crucial factor for the outcome of emergencies, especially those that involve human lives.
This paper looks at Lisbon’s firefighter’s occurrences and presents a model, based on city characteristics and climacteric data, to predict whether there will be an occurrence at a certain location, according to the weather forecasts. In this study three algorithms were considered, Logistic Regression, Decision Tree and Random Forest, as well as four techniques to balance the data – random over-sampling, SMOTE, random under-sampling and Near Miss –, which were compared to the baseline, the imbalanced data.
Measured by the AUC, the best performant model was a random forest with random under-sampling at 0.68. This model was well adjusted across the city and showed that precipitation and size of the subsection are the most relevant features in predicting firefighter’s occurrences.
The work presented here has clear implications on the firefighter’s decision-making regarding vehicle allocation, as now they can make an informed decision considering the predicted occurrences.
Original languageEnglish
QualificationMaster of Science
Awarding Institution
  • NOVA Information Management School (NOVA IMS)
Supervisors/Advisors
  • Neto, Miguel de Castro, Supervisor
Award date7 Jan 2020
Publication statusPublished - 7 Jan 2020

Keywords

  • Emergency response
  • Firefighters
  • Predictive modeling
  • Smart city

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