Predicting fire spread and behaviour on the fireline. Wildfire analyst pocket: A mobile app for wildland fire prediction

Monedero, Santiago; Ramírez, Joaquin; Cardíl, Adrián

doi:10.1016/j.ecolmodel.2018.11.016

Cited by 41 publications

(32 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of the bivariate and multivariate techniques are sensitive to the inclusion of the collinear variables, as these intercorrelated variables can significantly reduce the accuracy of the model [7]. Thus, before model building, the highly collinear variables should be identified and excluded from further procedures [7][8][9][10][11][12][13][14][15][16]. To examine possible collinearity among the variables used in this study, we computed the variance inflation factor (VIF) and tolerance and checked their critical values for all variables.…”

Section: Multicollinearity Assessmentmentioning

confidence: 99%

“…In an effort to mitigate the effects of the wildfires, managers delineate fire-prone landscapes for allocating suppression resources and firefighting efforts [7], often based on the fire management systems [8]. Various fire management and prevention systems have been suggested and developed in different countries such as USA, Canada, Spain, Portugal, and Australia [9,10] that have originated from attempts to satisfy a growing demand for fire prevention in the fire-prone landscapes [7]. These systems are usually based on a predictive model which aims at providing near-time wildfire predictions up to 10-15 years into the future [7,11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

et al. 2019

View full text Add to dashboard Cite

Wildfires are one of the most common natural hazards worldwide. Here, we compared the capability of bivariate and multivariate models for the prediction of spatially explicit wildfire probability across a fire-prone landscape in the Zagros ecoregion, Iran. Dempster–Shafer-based evidential belief function (EBF) and the multivariate logistic regression (LR) were applied to a spatial dataset that represents 132 fire events from the period of 2007–2014 and twelve explanatory variables (altitude, aspect, slope degree, topographic wetness index (TWI), annual temperature, and rainfall, wind effect, land use, normalized difference vegetation index (NDVI), and distance to roads, rivers, and residential areas). While the EBF model successfully characterized each variable class by four probability mass functions in terms of wildfire probabilities, the LR model identified the variables that have a major impact on the probability of fire occurrence. Two distribution maps of wildfire probability were developed based upon the results of each model. In an ensemble modeling perspective, we combined the two probability maps. The results were verified and compared by the receiver operating characteristic (ROC) and the Wilcoxon Signed-Rank Test. The results showed that although an improved predictive accuracy (AUC = 0.864) can be achieved via an ensemble modeling of bivariate and multivariate statistics, the models fail to individually provide a satisfactory prediction of wildfire probability (EBFAUC = 0.701; LRAUC = 0.728). From these results, we recommend the employment of ensemble modeling approaches for different wildfire-prone landscapes.

show abstract

Section: Multicollinearity Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

et al. 2019

View full text Add to dashboard Cite

show abstract

“…These simulators take into account the description of model inputs (e.g. wind speed, fuel moisture, fuel type, slope steepness) as they vary across the landscape and can reduce the time taken to produce a detailed fire growth simulation when compared to manual methods involving graphs, tables and software applications (Rothermel 1983(Rothermel , 1991Andrews et al 2008;Plucinski et al 2017;Taylor and Alexander 2018;Monedero et al 2019).…”

Section: Introductionmentioning

confidence: 99%

The 10% wind speed rule of thumb for estimating a wildfire’s forward rate of spread in forests and shrublands

Cruz

Alexander

2019

Annals of Forest Science

View full text Add to dashboard Cite

& Key message The collective analysis of a relatively large number of wildfire observations documented in conifer forests, dry eucalypt forests and temperate shrublands revealed that the forward rate of fire spread is roughly 10% of the average 10-m open wind speed, provided both are expressed in the same units (e.g. km h −1). & Context Knowledge of a wildfire's forward spread rate is a prerequisite for defining adequate fire suppression strategies and to ensure timely public warnings. & Aims We wanted to investigate the possibility that a simple relationship exists that could be used as a first approximation for quickly estimating a wildfire's spread rate simply from the open wind speed alone. & Methods We analysed data from a number of high-intensity wildfire observations (n = 118) documented in temperate shrublands, Australian dry eucalypt forests and North American conifer forests to examine the suitability and soundness of a relationship between wind speed and rate of fire spread. We also contrasted the performance of the best function against established fire spread rate models for the three fuel types. & Results The resulting rule of thumb is that the forward rate of spread of wildfires burning in forests and shrublands in relatively dry conditions is approximately equal to 10% of the average 10-m open wind speed, where both values are expressed in the same units. & Conclusion The rule of thumb gives the most accurate results for dry fuel and high wind speed conditions with reduced bias and mean relative errors lower than 50%. Under these conditions, the error statistics are comparable to those obtained by the established fire spread rate models. The rule is not applicable to grasslands.

show abstract

“…Wildfire simulation models can help to better understand the dynamics of wildfires and forecasting their propagationwhich is extremely important in strategizing an effective disaster response. Some models are designed for or better suited to use in pre-fire assessment and planning [88] and others for real-time incident decision support [89]. In this context, [14] analyzed and compared the performance of 23 simulators applicable in forecasting wildfire propagations.…”

Section: A Wildfire Simulatorsmentioning

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

Predicting fire spread and behaviour on the fireline. Wildfire analyst pocket: A mobile app for wildland fire prediction

Cited by 41 publications

References 20 publications

Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

The 10% wind speed rule of thumb for estimating a wildfire’s forward rate of spread in forests and shrublands

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

Contact Info

Product

Resources

About