Optimizing watchtower locations for forest fire monitoring using location models

Bao, Shitai; Xiao, Ningchuan; Lai, Zehui; He-yuan, Zhang; Kim, Chang-Joo

doi:10.1016/j.firesaf.2014.11.016

Cited by 93 publications

(75 citation statements)

References 23 publications

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“…A questão crítica em sistemas de monitoramento de incêndios florestais é a resposta imediata a fim de minimizar a escala da destruição (BAO et al, 2015). Neste estudo, observou-se um tempo médio para o ataque de 194,82 minutos (classe IV) para uma área queimada média de 613,61 ha em UCs no Brasil (Tabela 6).…”

Section: Discussionunclassified

Avaliação Da Eficiência De Combate Aos Incêndios Florestais Em Unidades De Conservação Brasileiras

Félix

Lima

Torres

et al. 2018

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ResumoO objetivo deste trabalho foi avaliar a eficiência de combate aos incêndios florestais em unidades de conservação brasileiras no período de 2008 a 2012. Foram utilizados três parâmetros principais para se avaliar a eficiência de combate: a área queimada, em que se avaliou a eficiência, distribuindo os incêndios ocorridos por classes de tamanho, e se observou a área queimada média por incêndio; o tempo para o ataque e sua distribuição por classe de tempo, em que se avaliou a eficiência de mobilização dos combatentes em termos Abstract Assessment of firefighting efficiency in Brazilian conservation units.The objective of this study was to assess the firefighting efficiency in Brazilian conservation units from 2008 to 2012. Three main parameters were used to evaluate the combat efficiency: burned area, in which efficiency was evaluated, the fires were distributed by size classes, and the average burned area by fire was observed; time for attack and its distribution by time class, in which the firefighters mobilization efficiency was evaluated in terms of average time from detection until the first fire attack; and time of combat, in which the average time of combat and its distribution by class of time of combat were evaluated. In the analysed period, Brazil presented a low combat efficiency. The average burned area in Brazilian protected areas was 613.61 ha in this period. The average time for attack and the average time of combat were 194.82 and 1,012.51 minutes, respectively. The main difficulty in the study was the lack of complete data of the Record of Fire Occurrence (ROI). Keywords: Fire, Brazil, conservation units, ROI. INTRODUÇÃOIncêndios resultam em mudanças drásticas em todos os tipos de ecossistemas florestais (ZAITSEV et al., 2016). Eles têm forte impacto tanto sobre as condições bióticas como as abióticas (ADÁMEK et al., 2015). Nas Unidades de Conservação (UC), áreas ambientais destinadas primordialmente a proteção, os incêndios florestais são uma grave perturbação, que pode colocar em risco os processos ambientais e a biodiversidade. Na grande maioria das UCs brasileiras, não existe um planejamento adequado visando à prevenção e ao combate a incêndios (BONTEMPO et al., 2011).Grande parte dessa dificuldade no planejamento está relacionada a fatores incertos e altamente variáveis, tais como condições meteorológicas, ação antrópica, comportamento do fogo e propagação e seus efeitos. Estes são a base da gestão de incêndios e decisões políticas, através de múltiplos níveis e escalas, e são responsáveis pela eficiência dos recursos destinados ao combate e supressão (PACHECO et al., 2015). Em contrapartida, com uma quantidade limitada de equipamentos e recursos financeiros e humanos, os gestores florestais devem decidir a aplicação mais eficiente de subsídios em ações, como prevenção (ex. campanhas de educação ambiental), gestão de combustível (ex. queima prescrita e tratamentos mecânicos), pré-supressão (ex. planejamento e preparação,

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Section: Discussionunclassified

Avaliação Da Eficiência De Combate Aos Incêndios Florestais Em Unidades De Conservação Brasileiras

Félix

Lima

Torres

et al. 2018

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“…Local “neighborhoods” were then populated around these selected candidate sites, by randomly selecting candidate sites within their nearby vicinities. The result is a set of candidate site locations that resembles real‐world sets typically determined after performing terrain analysis to identify suitable site locations according to specific criteria, for example, degree of slope (Heyns and Van Vuuren ), terrain feature selection (Kim, Rana, and Wise ; Bao et al ) and problem‐specific search procedures (Heyns and Van Vuuren ). Three “features” were manually selected for the purpose of center objectives and are indicated by crosses in Fig.…”

Section: Implementation Examplesmentioning

confidence: 99%

“…This is certainly the case for real‐world facility location problems that involve the placement of observation/detection equipment with multiple covering objectives (Murray ; Kim, Rana, and Wise ; Heyns and Van Vuuren ). In such instances, approximate algorithms are employed instead of exact solution methodologies to provide a set of approximately optimal solution alternatives within a reasonable amount of time (Zitzler, Deb, and Thiele ; Deb et al ; Kim, Rana, and Wise , ; Bao et al ; Heyns and Van Vuuren ) . In this article, we introduce a novel MTZ implementation of the popular multi‐objective approximate solution methodology which is based on the non‐dominated sorting genetic algorithm‐II (NSGA‐II) (Deb et al ) and is capable of solving problem instances involving multiple objectives.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐Type, Multi‐Zone Facility Location

Heyns

Vuuren

2017

Geographical Analysis

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The placement of facilities according to spatial and/or geographic requirements is a popular problem within the domain of location science. Objectives that are typically considered in this class of problems include dispersion, median, center, and covering objectives—and are generally defined in terms of distance or service‐related criteria. With few exceptions, the existing models in the literature for these problems only accommodate one type of facility. Furthermore, the literature on these problems does not allow for the possibility of multiple placement zones within which facilities may be placed. Due to the unique placement requirements of different facility types—such as suitable terrain that may be considered for placement and specific placement objectives for each facility type—it is expected that different suitable placement zones for each facility type, or groups of facility types, may differ. In this article, we introduce a novel mathematical treatment for multi‐type, multi‐zone facility location problems. We derive multi‐type, multi‐zone extensions to the classical integer‐linear programming formulations involving dispersion, centering and maximal covering. The complexity of these formulations leads us to follow a heuristic solution approach, for which a novel multi‐type, multi‐zone variation of the non‐dominated sorting genetic algorithm‐II algorithm is proposed and employed to solve practical examples of multi‐type, multi‐zone facility location problems.

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“…A case study involving data provided by the municipality of Milan is presented in [5]. Naturally, the techniques used to optimise station locations can be applied in many other contexts a well -e.g., placement of defibrillators [22], fire watchtowers [2] or electric taxi charging stations [23] -see also the overview presented in the section on location-allocation models in [10]. For a general overview of location-allocation modelling of bike sharing systems can be found in [24].…”

Section: Background and Related Workmentioning

confidence: 99%

Probabilistic Modelling of Station Locations in Bicycle-Sharing Systems

Reijsbergen

2016

Lecture Notes in Computer Science

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Abstract. We present a simulation methodology for generating the locations of stations in Bicycle-Sharing Systems. We present several methods that are inspired by the literature on spatial point processes. We evaluate how the artificially generated systems compare to existing systems through a case study involving 11 cities worldwide. The method that is found to perform best is a data-driven approach in which we use a dataset of places of interest in the city to 'rate' how attractive city areas are for station placement. The presented methods use only non-proprietary data readily available via the Internet.

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Optimizing watchtower locations for forest fire monitoring using location models

Cited by 93 publications

References 23 publications

Avaliação Da Eficiência De Combate Aos Incêndios Florestais Em Unidades De Conservação Brasileiras

Avaliação Da Eficiência De Combate Aos Incêndios Florestais Em Unidades De Conservação Brasileiras

Multi‐Type, Multi‐Zone Facility Location

Probabilistic Modelling of Station Locations in Bicycle-Sharing Systems

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