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.
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 language | English |
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Qualification | Master of Science |
Awarding Institution |
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Supervisors/Advisors |
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Award date | 7 Jan 2020 |
Publication status | Published - 7 Jan 2020 |
Keywords
- Emergency response
- Firefighters
- Predictive modeling
- Smart city