A review of wildland fire spread modelling, 1990-present 3: Mathematical analogues and simulation models

In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behvaiour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of a simulation or mathematical analogue nature. Most simulation models are implementations of existing empirical or quasi-empirical models and their primary function is to convert these generally one dimensional models to two dimensions and then propagate a fire perimeter across a modelled landscape. Mathematical analogue models are those that are based on some mathematical conceit (rather than a physical representation of fire spread) that coincidentally simulates the spread of fire. Other papers in the series review models of an physical or quasi-physical nature and empirical or quasi-empirical nature. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.Comment: 20 pages + 9 pages references + 1 page figures. Submitted to the International Journal of Wildland Fir

Cellular automata and self-organized criticality

Cellular automata provide a fascinating class of dynamical systems capable of diverse complex behavior. These include simplified models for many phenomena seen in nature. Among other things, they provide insight into self-organized criticality, wherein dissipative systems naturally drive themselves to a critical state with important phenomena occurring over a wide range of length and time scales.Comment: 23 pages, 12 figures (most in color); uses sprocl.tex; chapter submitted for "Some new directions in science on computers," G. Bhanot, S. Chen, and P. Seiden, ed

Discrete event front tracking simulator of a physical fire spread model

International audienceSimulation of moving interfaces, like a fire front usually requires the resolution of a large scale and detailed domain. Such computing involves the use of supercomputers to process the large amount of data and calculations. This limitation is mainly due to the fact that large scale of space and time is usually split into nodes, cells or matrices, and the solving methods often require small time steps. This paper presents a novel method that enables the simulation of large scale/high resolution systems by focusing on the interface. Unlike the conventional explicit and implicit integration schemes, it is based on the discrete-event approach, which describes time advance in terms of increments of physical quantities rather than discrete time stepping. Space as well is not split into discrete nodes or cells, but we use polygons with real coordinates. The system is described by the behaviour of its interface, and evolves by computing collision events of this interface in the simulation. As this simulation technique is suited for a class of models that can explicitly provide rate of spread for a given configuration, we developed a specific radiation based propagation model of physical wildland fire. Simulations of a real large scale fire performed with an implementation of our method provide very interesting results in less than 30 seconds with a 3 metres resolution with current personal computers

The identification of cellular automata

Although cellular automata have been widely studied as a class of the spatio temporal systems, very few investigators have studied how to identify the CA rules given observations of the patterns. A solution using a polynomial realization to describe the CA rule is reviewed in the present study based on the application of an orthogonal least squares algorithm. Three new neighbourhood detection methods are then reviewed as important preliminary analysis procedures to reduce the complexity of the estimation. The identification of excitable media is discussed using simulation examples and real data sets and a new method for the identification of hybrid CA is introduced

A semi-empirical cellular automata model for wildfire monitoring from a geosynchronous space platform

The environmental and human impacts of wildfires have grown considerably in recent years due to an increase in their frequency and coverage. Effective wildfire management and suppression requires real-time data to locate fire fronts, model their propagation and assess the impact of biomass burning. Existing empirical wildfire models are based on fuel properties and meteorological data with inadequate spatial or temporal sampling. A geosynchronous space platform with the proposed set of high resolution infrared detectors provides a unique capability to monitor fires at improved spatial and temporal resolutions. The proposed system is feasible with state-of-the-art hardware and software for high sensitivity fire detection at saturation levels exceeding active flame temperatures. Ground resolutions of 100 meters per pixel can be achieved with repeat cycles less than one minute. Atmospheric transmission in the presence of clouds and smoke is considered. Modeling results suggest fire detection is possible through thin clouds and smoke. A semi-empirical cellular automata model based on theoretical elliptical spread shapes is introduced to predict wildfire propagation using detected fire front location and spread rate. Model accuracy compares favorably with real fire events and correlates within 2% of theoretical ellipse shapes. This propagation modeling approach could replace existing operational systems based on complex partial differential equations. The baseline geosynchronous fire detection system supplemented with a discrete-based propagation model has the potential to save lives and property in the otherwise uncertain and complex field of fire management

FORFIS: A forest fire firefighting simulation tool for education and research

We present a forest fire firefighting simulation tool named FORFIS that is implemented in Python. Unlike other existing software, we focus on a user-friendly software interface with an easy-to-modify software engine. Our tool is published under GNU GPLv3 license and comes with a GUI as well as additional output functionality. The used wildfire model is based on the well-established approach by cellular automata in two variants - a rectangular and a hexagonal cell decomposition of the wildfire area. The model takes wind into account. In addition, our tool allows the user to easily include a customized firefighting strategy for the firefighting agents.Comment: 5 figure