Modelling fire spread through fuel beds

Weber, R. O.

doi:10.1016/0360-1285(91)90003-6

Cited by 172 publications

(88 citation statements)

References 25 publications

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“…Numerous mathematical models, computerized decision support systems, and guides have come to be developed for predicting wildland fire behaviour as documented in earlier reviews by Catchpole and de Mestre (1986), Weber (1991), Perry (1998), Pastor et al (2003) and more recently by Sullivan (2009a,b,c). Fire behaviour models are typically distinguished into two main categories: (1) physical and (2) empirical or semi-empirical models.…”

Section: A Brief Historical Sketch Of Developments In Wildland Fire Bmentioning

confidence: 99%

Limitations on the accuracy of model predictions of wildland fire behaviour: A state-of-the-knowledge overview

AlexanderMartin

Cruz

2013

The Forestry Chronicle

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The degree of accuracy in model predictions of wildland fire behaviour characteristics are dependent on the model's applicability to a given situation, the validity of the model's relationships, and the reliability of the model input data. While much progress has been made by fire behaviour research in the past 35 years or so in addressing these three sources of model error, the accuracy in model predictions are still very much at the mercy of our present understanding of the natural phenomena exhibited by free-burning wildland fires and the inherent temporal and spatial variability in the fire environment. This paper will serve as a state-of-the-art primer on the subject of error sources in model predictions of wildland fire behaviour and includes a short historical overview of wildland fire behaviour research as it relates to model development.

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Section: A Brief Historical Sketch Of Developments In Wildland Fire Bmentioning

confidence: 99%

Limitations on the accuracy of model predictions of wildland fire behaviour: A state-of-the-knowledge overview

AlexanderMartin

Cruz

2013

The Forestry Chronicle

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“…Modeling of fireline evolution was reviewed by [94]. [2,3,38] derive fire spread rates without using reaction kinetics.…”

Section: Fireline Evolution Fire Spread and Empirical Modelsmentioning

confidence: 99%

A wildland fire model with data assimilation

Mandel

Bennethum

Beezley

et al. 2008

Mathematics and Computers in Simulation

141

107

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A wildfire model is formulated based on balance equations for energy and fuel, where the fuel loss due to combustion corresponds to the fuel reaction rate. The resulting coupled partial differential equations have coefficients that can be approximated from prior measurements of wildfires. An ensemble Kalman filter technique with regularization is then used to assimilate temperatures measured at selected points into running wildfire simulations. The assimilation technique is able to modify the simulations to track the measurements correctly even if the simulations were started with an erroneous ignition location that is quite far away from the correct one.

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“…That's why, the theory of Modelling and Simulation [1] is usually applied to describe the physical mechanisms of fire spreading systems. As discussed in literature, three main categories of (CA) modelling strategies are commonly used in this context [2,3]: (1) physics-based or physical, (2) statistical, and (3) empirical or semi-empirical.…”

Section: Introductionmentioning

confidence: 99%

Randomizing activity in fire spreading simulations

Innocenti

Cancellieri²

2013

ITM Web of Conferences

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Abstract. In this paper, we suggest the use of activity paradigm to model the uncertain and imprecise nature of the local dynamics in fire spreading simulations. Formerly based on a very simple set of rules, Cellular Automata (CA) models are remarkable in their ability to easily simulate fire spreading behaviour. A new algorithm to track activity concept in a (CA) model is presented. It is called random-activity and it combines activity and stochastic precepts. This algorithm enhances the realism of the spatial simulations. It resolves the problem of the spurious symmetries of the square lattice usually encountered which affect negatively outputs. Experiment shows that the approach enhances fire spreading simulations which are based on local description of the fire dynamics with general descriptive laws. A comparison between classical and random-activity algorithms is performed. The random-activity algorithm proposed outperforms the classical algorithm in terms of graphical restitutions' realism. It might be used to develop fire riskmanagement tools. A new way is presented to combine activity concept and stochastic modelling in a (CA) model.

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Modelling fire spread through fuel beds

Cited by 172 publications

References 25 publications

Limitations on the accuracy of model predictions of wildland fire behaviour: A state-of-the-knowledge overview

Limitations on the accuracy of model predictions of wildland fire behaviour: A state-of-the-knowledge overview

A wildland fire model with data assimilation

Randomizing activity in fire spreading simulations

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