Predicting fires for policy making: Improving accuracy of fire brigade allocation in the Brazilian Amazon

Morello, Thiago Fonseca; Ramos, Rossano Marchetti; Anderson, Liana O.; Owen, Nathan; Rosan, Thais M.; Steil, Lara

doi:10.1016/j.ecolecon.2019.106501

Cited by 31 publications

(28 citation statements)

References 88 publications

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“…The influence of anthropogenic factors on forest fires can be analyzed in two ways; in relation to fire events treated as points or in relation to their number per area unit. For arealevel analyses, the reference unit used may be a grid network [13,14,29] or administrative units [30,33,51,52]. The first area-related approach may obtain more accurate results, unfortunately it cannot be applied for many socio-economic variables, because these statistics are usually compiled for administrative units.…”

Section: Anthropogenic Factors Potentially Related To Forest Firesmentioning

confidence: 99%

Human Activity Affects Forest Fires: The Impact of Anthropogenic Factors on the Density of Forest Fires in Poland

Kolanek

Szymanowski

Raczyk

2021

Forests

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Forest fires, due to climate change, are a growing threat to human life, health, and property, especially in temperate climates. Unfortunately, the impact of individual factors on forest fires varies, depending on the geographical region and its natural and socio-economic conditions. The latter are rarely introduced into fire warning systems, which significantly reduces their effectiveness. Therefore, the main goal of this study was to quantify the impact of a wide range of anthropogenic factors on forest fires, using Poland as a representative example of a Central European country. Data were analyzed in relation to districts for the period 2007–2017, using correlation analysis and regression modeling applying global and local/mixed regression methods. It was found that almost all of the 28 variables taken for analysis significantly determined the density of forest fires, but the greatest role was played by the length of the border between forests and built-up areas, and road density. In addition, the impact of most of the analyzed variables on forest fires varied over the study area, so implementing non-stationarity in geographically weighted regression models significantly improved the goodness-of-fit compared to global models.

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Section: Anthropogenic Factors Potentially Related To Forest Firesmentioning

confidence: 99%

Human Activity Affects Forest Fires: The Impact of Anthropogenic Factors on the Density of Forest Fires in Poland

Kolanek

Szymanowski

Raczyk

2021

Forests

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“…South America, especially the Amazon region, has already been identified as a high-risk region where land-use and climate change could have a synergistic impact on future fires (Le Page, Morton, Hartin, et al, 2017). In this region, fires are set in deforested areas (van Marle et al, 2017) to renovate cultivated land and clear forests for agricultural use (Morello et al, 2020;Shimabukuro et al, 2019).…”

Section: Regions With High Ddf Riskmentioning

confidence: 99%

“…To reduce the risk of DDF in these areas, it is necessary to develop effective fire prevention policies or measures. These countries could provide subsidies for firefree land management practices, educate farmers on the importance of fires, and implement real-time fire monitoring (Morello et al, 2017(Morello et al, , 2020. Implementing these policies and measures will require a greater understanding of fire regimes, particularly of how they are likely to change in the future.…”

Section: Regions With High Ddf Riskmentioning

confidence: 99%

How Will Deforestation and Vegetation Degradation Affect Global Fire Activity?

et al. 2021

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“…For this, the Data Envelopment Analysis (DEA) and Group-number Efficiency Scale Approach (GESA) techniques were used with input variables as the number of on-duty personnel, on-duty cost, number of fires engines and vehicle maintenance fee. In 2020, Morello et al [5] developed seven econometric models for fire prediction in the Brazilian Amazon with the aim of establishing suppression policies for forest fires. Four of the models with the lowest RMSE obtained a percentage error of no more than 29%.…”

Section: Related Workmentioning

confidence: 99%

Predicting Fire Brigades Operational Breakdowns: A Real Case Study

et al. 2020

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Over the years, fire departments have been searching for methods to identify their operational disruptions and establish strategies that allow them to efficiently organize their resources. The present work develops a methodology for breakage calculation and another for predicting disruptions based on machine learning techniques. The main objective is to establish indicators to identify the failures due to the temporal state of the organization in the human and vehicular material. Likewise, by forecasting disruptions, to determine strategies for the deployment or acquisition of the necessary armament. This would allow improving operational resilience and increasing the efficiency of the firemen over time. The methodology was applied to the Departmental Fire and Rescue Doubs (SDIS25) in France. However, it is generic enough to be extended and adapted to other fire departments. Considering a historic of breakdowns of 2017 and 2018, the best predictions of public service breakdowns for the year 2019, presented a root mean squared error of 2.5602 and a mean absolute error of 2.0240 on average with the XGBoost technique.

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Predicting fires for policy making: Improving accuracy of fire brigade allocation in the Brazilian Amazon

Cited by 31 publications

References 88 publications

Human Activity Affects Forest Fires: The Impact of Anthropogenic Factors on the Density of Forest Fires in Poland

Human Activity Affects Forest Fires: The Impact of Anthropogenic Factors on the Density of Forest Fires in Poland

How Will Deforestation and Vegetation Degradation Affect Global Fire Activity?

Predicting Fire Brigades Operational Breakdowns: A Real Case Study

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