Publicação
Optimization of firefighter response with predictive analytics : practical application to Lisbon, Portugal
| dc.contributor.advisor | Zejnilović, Leid | |
| dc.contributor.advisor | Neto, Miguel de Castro Simões Ferreira | |
| dc.contributor.author | Teixeira, Leonor Pimentel Perestrelo Braz | |
| dc.date.accessioned | 2021-03-04T17:37:04Z | |
| dc.date.available | 2021-03-04T17:37:04Z | |
| dc.date.issued | 2020-01-07 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence | pt_PT |
| dc.description.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. | pt_PT |
| dc.identifier.tid | 202661326 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/113089 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Emergency response | pt_PT |
| dc.subject | Firefighters | pt_PT |
| dc.subject | Predictive modeling | pt_PT |
| dc.subject | Smart city | pt_PT |
| dc.title | Optimization of firefighter response with predictive analytics : practical application to Lisbon, Portugal | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Gestão de Informação, especialização em Gestão do Conhecimento e Inteligência de Negócio (Business Intelligence) | pt_PT |
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