Wildfire Likelihood’s Elements: A Literature Review

Mhawej, Mario; Faour, Ghaleb; Adjizian-Gérard, Jocelyne

doi:10.3390/challe6020282

Cited by 38 publications

(25 citation statements)

References 55 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a study regarding the likelihood of wildfires, Mhawej et al [43] listed 28 factors categorized as climatic, topographic, in situ, historical, and anthropogenic. The present study is more confined, since it considers structural wooden structures in very dry condition fires compared to normal conditions.…”

Section: Discussionmentioning

confidence: 99%

Cold Climate Structural Fire Danger Rating System?

Metallinou

Log

2018

Challenges

View full text Add to dashboard Cite

Worldwide, fires kill 300,000 people every year. The fire season is usually recognized to be in the warmer periods of the year. Recent research has, however, demonstrated that the colder season also has major challenges regarding severe fires, especially in inhabited (heated) wood-based structures in cold-climate areas. Knowledge about the effect of dry cellulose-based materials on fire development, indoor and outdoor, is a motivation for monitoring possible changes in potential fire behavior and associated fire risk. The effect of wind in spreading fires to neighboring structures points towards using weather forecasts as information on potential fire spread behavior. As modern weather forecasts include temperature and relative humidity predictions, there may already be sufficient information available to develop a structural fire danger rating system. Such a system may include the following steps: (1) Record weather forecasts and actual temperature and relative humidity inside and outside selected structures; (2) Develop a meteorology-data-based model to predict indoor relative humidity levels; (3) Perform controlled drying chamber experiments involving typical hygroscopic fire fuel; (4) Compare the results to the recorded values in selected structures; and (5) Develop the risk model involving the results from drying chamber experiments, weather forecasts, and separation between structures. Knowledge about the structures at risk and their use is also important. The benefits of an automated fire danger rating system would be that the society can better plan for potentially severe cold-climate fires and thereby limit the negative impacts of such fires.

show abstract

Section: Discussionmentioning

confidence: 99%

Cold Climate Structural Fire Danger Rating System?

Metallinou

Log

2018

Challenges

View full text Add to dashboard Cite

show abstract

“…While wind speed in uences the rapidity and extent of the spread of forest res (Tošić et al, 2019). Vegetational features like vegetation type, Normalized Vegetation Difference Index (NDVI), tree cover fraction; human-induced features like proximity to the road network, human habitation, or Wildland-Urban Interface (WUI); and in-situ factors like soil moisture, soil texture and fuel density have also been used for forest re prediction (Gheshlaghi et al, 2020;Jaafari et al, 2018;Mhawej et al, 2015;Satir et al, 2016) A forest re or wild re prediction map has become a valuable tool for disaster management and ecological restoration. Multicriteria decision analysis such as Analytic Hierarchy Process (AHP) (Ljubomir et al, 2019), Analytical Network Process (ANP) (Gheshlaghi et al, 2020;Regodic et al, 2018;Yathish et al, 2019) and other forms of expert opinion based methods (Goleiji et al, 2017) have been applied in forest re prediction mapping.…”

Section: Introductionmentioning

confidence: 99%

Maximum entropy-based forest fire likelihood mapping: analysing the trends, distribution, and drivers of forest fires in Sikkim Himalaya

Banerjee

2021

Scandinavian Journal of Forest Research

View full text Add to dashboard Cite

The recent episodes of forest res in Brazil and Australia of 2019 are tragic reminders of the hazards of forest re. Globally incidents of forest re events are on the rise due to human encroachment into the wilderness and climate change. Sikkim with a forest cover of more than 47%, suffers seasonal instances of frequent forest re during the dry winter months. To address this issue, a GIS-aided and MaxEnt machine learning-based forest re prediction map has been prepared using a forest re inventory database and maps of environmental features. The study indicates that amongst the environmental features, climatic conditions and proximity to roads are the major determinants of forest res. Model validation criteria like ROC curve, correlation coe cient, and Cohen's Kappa show a good predictive ability (AUC = 0.95, COR = 0.81, κ = 0.78). The outcomes of this study in the form of a forest re prediction map can aid the stakeholders of the forest in taking informed mitigation measures.

show abstract

“…There is a wide range of review work in various aspects of wildfire management available in the literature, including [12][13][14][15][16][17][18][19], among others. However, there is a limited number of review papers on wildfire management in relation to power systems.…”

Section: Introductionmentioning

confidence: 99%

Three Lines of Defense for Wildfire Risk Management in Electric Power Grids: A Review

et al. 2021

View full text Add to dashboard Cite

Wildfires pose a significant challenge to the natural and the built environments, as well as the safety and economic wellbeing of the communities residing in wildfire-prone areas. The electric power grid is specifically among the built environments most affected by, and contributing to, wildfires. In this paper, we propose a three lines of defense (3LD) framework for wildfire risk management in electric power infrastructure and review the literature from this lens. An overview of the physics and phenomenology of the wildfires as it relates to power grids is presented, and the logic for the proposed 3LD framework is discussed. The reviewed literature based on the 3LD theme includes the most relevant and emerging research work on wildfire prevention as the first line of defense, wildfire mitigation and proactive response as the second line of defense, and wildfire recovery preparedness as the third line of defense. This study reveals that while the state of the art, to a large extent, stands comprehensive in various aspects of power system resilience and wildfire risk management, there is a gap in the literature in addressing this emerging risk in a holistic, interdisciplinary approach.

show abstract

Wildfire Likelihood’s Elements: A Literature Review

Cited by 38 publications

References 55 publications

Cold Climate Structural Fire Danger Rating System?

Cold Climate Structural Fire Danger Rating System?

Maximum entropy-based forest fire likelihood mapping: analysing the trends, distribution, and drivers of forest fires in Sikkim Himalaya

Three Lines of Defense for Wildfire Risk Management in Electric Power Grids: A Review

Contact Info

Product

Resources

About