An Evaluation of Wildfire Vulnerability in the Wildland–Urban Interfaces of Central Portugal Using the Analytic Network Process

Nunes, Adélia; Figueiredo, Albano; Pinto, Carlos; Lourenço, Luciano

doi:10.3390/fire6050194

Cited by 2 publications

(2 citation statements)

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“…The two municipalities studied are located in the extensive territory that Lourenço et al [55] define as favourable to the cyclical progression of large forest fires, thanks to natural features such as orography and vegetation cover, but also due to human influence. In the central region, these areas include the more mountainous and inland areas [48,56]. Because of the topographical conditions, the type of occupation, which is predominantly forest/woodland, and sociodemographic characteristics, the cases studied belong to the group of municipalities with the highest hazard [48] as well as vulnerability to wildfires [56].…”

Section: Discussionmentioning

confidence: 99%

“…In the central region, these areas include the more mountainous and inland areas [48,56]. Because of the topographical conditions, the type of occupation, which is predominantly forest/woodland, and sociodemographic characteristics, the cases studied belong to the group of municipalities with the highest hazard [48] as well as vulnerability to wildfires [56].…”

Section: Discussionmentioning

confidence: 99%

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Planning Wildfire Evacuation in the Wildland–Urban Interfaces of Central Portugal

Nunes,

Pinto,

Figueiredo

et al. 2024

Fire

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In recent decades, wildfires have become common disasters that threaten people’s lives and assets, particularly in wildland–urban interfaces (WUIs). Developing an effective evacuation strategy for a WUI presents challenges to emergency planners because of the spatial variations in biophysical hazards and social vulnerability. The aim of this study was to map priority WUIs in terms of evacuation. The factors considered were the seriousness of the risk of wildfire exposure, and the population centres whose greatest constraints on the evacuation process stemmed from the nature of the exposed population and the time required to travel to the nearest shelter/refuge. An integrated framework linking wildfire hazard, social vulnerability, and the time taken to travel by foot or by car to the nearest refuge/shelter was applied. The study area includes two municipalities (Lousã and Sertã) in the mountainous areas of central Portugal that are in high-wildfire-risk areas and have very vulnerable and scattered pockets of exposed population. The combination of wildfire risk and travelling time to the nearest shelters made it possible to identify 20% of the WUIs that were priority areas for evacuation in the case of Sertã. In the case of Lousã, 3.4% were identified, because they were highly exposed to wildfire risk and had a travelling time to the nearest shelter of more than 15 min on foot. These results can assist in designing effective pre-fire planning, based on fuel management strategies and/or managing an effective and safe evacuation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Planning Wildfire Evacuation in the Wildland–Urban Interfaces of Central Portugal

Nunes,

Pinto,

Figueiredo

et al. 2024

Fire

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Spatial model of wildfire susceptibility using Machine Learning approaches on Rawa Aopa Watumohai National Park, Indonesia

Aldiansyah,

Madani

2024

GeoScape

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Rawa Aopa National Park has experienced a severe wildfire. These fires are affected by several factors, including topography, meteorology, vegetation, and source of fire. This study uses a Machine Learning approach based on re-sampling methods (e.g. crossvalidation, bootstrap, and random subsampling) to evaluate, and improve the performance of twelve basic Machine Learning algorithms: Generalized Linear Model, Support Vector Machine, Random Forest, Boosted Regression Trees, Classification And Regression Tree, Multivariate Adaptive Regression Splines, Mixture Discriminate Analysis, Flexible Discriminant Analysis, Maximum Entropy, Maximum Likelihood, Radial Basis Function, and Multi-Layer Perceptron, analyze the causes of wildfires, and the correlation between variables. The model is evaluated by Area Under Curve, Correlation, True Skill Statistics, and Deviance. The evaluation results show that Bt-RF has a good performance in predicting wildfire susceptibility in TNRAW with AUC=0.98, COR=0.96, TSS=0.97, and Deviance=0.15. An area of 644.88 km2 or the equivalent of 59.82% of the area is a wildfire susceptibility area with the concentration of fires occurring in the savanna ecosystem which is around 245.12 km2 or the equivalent of 88.95% of the jungle zone. Among the 17 parameters that cause fires, this area is strongly influenced by Maximum Temperature, Land Use Land Cover, and Distance from Road. There is a strong correlation between soil and distance from settlements = 0.96.

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An Evaluation of Wildfire Vulnerability in the Wildland–Urban Interfaces of Central Portugal Using the Analytic Network Process

Cited by 2 publications

References 101 publications

Planning Wildfire Evacuation in the Wildland–Urban Interfaces of Central Portugal

Planning Wildfire Evacuation in the Wildland–Urban Interfaces of Central Portugal

Spatial model of wildfire susceptibility using Machine Learning approaches on Rawa Aopa Watumohai National Park, Indonesia

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