Spatial modelling and mapping of urban fire occurrence in Portugal

Bispo, Regina; Vieira, Francisca G.; Bachir, Nadjet Larbi Daho; Espadinha-Cruz, Pedro; Lopes, José Pedro; Penha, Alexandre; Marques, Filipe J.; Grilo, António

doi:10.1016/j.firesaf.2023.103802

Cited by 10 publications

(3 citation statements)

References 39 publications

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“…In particular, Bispo et al . 16 model urban fire occurrences at a municipality level, ultimately mapping the probability of urban fires occurrence in mainland Portugal, and Eslamzadeh et al . 17 assessed fire departments performance using a slack-based Data Envelopment Analysis (DEA).…”

Section: Background and Summarymentioning

confidence: 99%

A decade of urban fires: Portuguese events between 2013 and 2022

Bispo,

Marques,

Penha

et al. 2023

Sci Data

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This study describes a dataset containing urban fire events that took place in mainland Portugal between 2013 and 2022. The Regulation n.º3317-A/2018, established by the Portuguese National Emergency and Civil Protection Authority (Autoridade Nacional de Emergência e Proteção Civil, ANEPC), defines the Operations Management System (Sistema de Gestão de Operações, SGO). Among other attributions, this system allows to manage the lyfe-cycle of the urban fire events, from ignition to extinction, through the Operations Decision Support System (Sistema de Apoio Ã Decisão Operacional, SADO). This system supports the systematic collection of a minimum set of data on each event. All instances included in the dataset were retrieved from SADO. To make the data suitable for analytic purposes, several pre-processing actions were taken, including the steps of data transformation and cleaning. The dataset was further validated by a set of technical procedures aiming to verify both data correctness and utility. The final dataset provides the most recent multi-year record of Portuguese urban fires including 27 variables on 72641 events.

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Section: Background and Summarymentioning

confidence: 99%

A decade of urban fires: Portuguese events between 2013 and 2022

Bispo,

Marques,

Penha

et al. 2023

Sci Data

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“…Fires in urban areas usually have severe consequences. High population density along with the complexity of the urban network has potentially significant effects in property loss, physical damage and loss of life (Bispo et al 2023). Urban and rural firefighting is still one of the most dangerous activities performed by fire services at the international level.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Comparing the performance of Genetic Algorithm and Particle Swarm Optimization Algorithm in allocating and scheduling fire stations for dispatching forces to a fire/accident (A Case study: the Region 19, Tehran, Iran)

Kheirdast,

Jozi,

Rezaian

et al. 2023

Preprint

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Considering the importance of "time" in the process of dispatching forces to reach the fire or accident site, GA or PSO models can be used as artificial intelligence alternatives. Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSOA) models can be used. This research shows which of these two models is more appropriate in this case study. With the hypothesis that GA and PSOA have positive effects on the allocation and scheduling of the stations, this research seeks to compare them in order to find which one of these two methods is more appropriate to shorten the time to reach fire/incident site in the Region 19 of Tehran. This is an applied type of research. Data analysis was carried out using NFPA standards and MATLAB software. The statistical population includes 8 fire stations and 250 personnel of the stations selected in a simple way, and the sampling volume was obtained using Morgan's table (n = 148). At first, the algorithm of dispatching forces to reach the site of fire/incident was designed and implemented based on PSOA, GA and the time to response the incident according to NFPA1720 standards. After writing the assumptions of the problem and running the mathematical model from nonlinear to linear, the data was entered into the MATLAB software, and finally by comparing the performance improvement of PSOA and GA, appropriate results were obtained. In order to efficiently assign and schedule fire stations to arrive at the site, a linear numerical programming model was presented with the aim of minimizing the arrival time and taking into account the effect of firemen's fatigue (α = 0.1). The findings of the research showed that the operation processing time (of fire extinguishing) had a normal distribution with a mean of 40 minutes and a variance of 10 minutes, independent of the severity of the incident. Also, fatigue coefficient was calculated 0.1 by analyzing the sensitivity of the solution time of the algorithm with changes [0–1]. The initial standard travel time, with an average speed of 47 km/h and a density factor of 1.24, was 5min:20s. Solving the problem in large and small dimensions showed that the initial power effect of each fire station is 0.36 according to the fatigue level of the forces. Based on the obtained results, GA performs better in terms of problem solution time, and the improved PSOA also has higher quality answers.

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“…A recent study in Chile [10] used Integer Programming (IP)-one of the most common RAFD models [9]-and Geographic Information System (GIS) analysis to optimize the number of FPS vehicles and their locations based on maximizing the coverage of expected FPS demand in FDs. Balancing the ratio of firefighters and populations of the cities in Thailand to provide more efficient FPS was the objective of a study [11] that applied an omit resource approach for human resource allocation and Data Envelopment Analysis (DEA) for FPS performance assessment Like other European countries, the FPS authority in Portugal-the National Authority for Emergency and Civil Protection (ANEPC))-faces challenges in delivering efficient fire protection services promptly [4,19,20]. This task of ensuring timely FPS delivery is a common challenge for the ANEPC, reflecting broader concerns shared by nations dealing with the complexities of urban fire management; to address it, multiple studies have been conducted in Portugal, including on optimizing the locations of the Portuguese fire stations [20], a model for the Fire Department Performance Assessment (FDPA) in Portuguese FDs [8], analyzing the urban fire in Portugal [19], assessing and mitigating fire risk in the Portuguese cities [21,22], urban resilience measures in Portuguese districts [23], and characterizations on the urban fire incidents in Portuguese cities [24].…”

Section: Introductionmentioning

confidence: 99%

All Lives Matter: A Model for Resource Allocation to Fire Departments in Portugal

K. Eslamzadeh,

Grilo,

Espadinha-Cruz

2024

Fire

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Optimizing Resource Allocation in Fire Departments (RAFD) is crucial for enhancing Fire Protection Services (FPS) and ultimately saving lives. Efficient RAFD ensures that fire departments have the necessary resources to respond effectively to emergencies. This paper presents a method for optimizing RAFD based on performance assessment results, examining its impact on Fire Department (FD) efficiency in Portugal. Evaluating data from 353 FDs, two RAFD optimization methods were assessed: one adhering to Portuguese regulations and constraints, such as budget allocation limitations, and another without such constraints. Integrating a slack-based data envelopment analysis model and mixed-integer linear programming, the study found that incorporating FD efficiency scores in RAFD improved overall efficiency at national, district, and FD levels. While adherence to Portuguese regulations led to balanced resource allocation and a 4% performance improvement at the national level, relaxing constraints yielded an 8% improvement, albeit with potential performance deterioration in some FDs. The detailed budget and efficiency metric analysis provided in this paper offers actionable insights for fire protection services enhancement. This underscores the importance of diverse optimization strategies to enhance FD efficiency, with implications for decision-makers at the Portuguese National Authority for Emergency and Civil Protection and similar organizations globally.

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Spatial modelling and mapping of urban fire occurrence in Portugal

Cited by 10 publications

References 39 publications

A decade of urban fires: Portuguese events between 2013 and 2022

A decade of urban fires: Portuguese events between 2013 and 2022

Comparing the performance of Genetic Algorithm and Particle Swarm Optimization Algorithm in allocating and scheduling fire stations for dispatching forces to a fire/accident (A Case study: the Region 19, Tehran, Iran)

All Lives Matter: A Model for Resource Allocation to Fire Departments in Portugal

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