Fire Propagation Model Research Articles

Spatiotemporal Integration of an Agent-Based First Responder Performance Model With a Fire Hazard Propagation Model for Probabilistic Risk Assessment of Nuclear Power Plants

Abstract To advance emergency response (ER) modeling in probabilistic risk assessment (PRA), this research offers a new methodology that explicitly incorporates the spatiotemporal evolution of underlying physical and social phenomena and their bidirectional interactions. While this methodology is applicable for various ER scenarios on different spatial and temporal scales, this paper focuses on advancing ER modeling for a nuclear power plant (NPP) internal fire. This paper provides a thorough review and categorization of existing studies on internal fire ER modeling for NPPs and highlights the contributions of this research. This paper then develops a new methodology for fire ER modeling by integrating an agent-based model of first responder performance (FRP) with a fire hazard propagation (FHP) model through a shared geographical information system (GIS)-based spatial simulation environment. This research is the first to explicitly incorporate space (in addition to time) into the FRP modeling within ER modeling of NPP fire probabilistic risk assessment (fire PRA). In addition, this research develops a GIS-based interface between FRP and FHP that has the capability of transferring both spatial and temporal information in a bidirectional way. Although this paper is focused on a fire ER scenario, the new methodology developed in this paper can contribute to modeling external control room (ExCR) human performance in other contexts, such as diverse and flexible coping strategy (FLEX), maintenance, and offsite first responders in level 3 PRA.

Stochastic decision trigger modelling to assess the probability of wildland fire impact

The ability to estimate both the time and probability of a wildfire reaching an area to be protected is critically important to preventing loss of human life and property, and damage to ecological and economic assets. Wildfire decision trigger modelling has been used to assess fire exposure and create evacuation trigger buffers around the communities providing a specific amount of warning time. This approach has been applied in multiple scenarios including household-level and community-level evacuation planning and during suppression operations. However, little attention has been paid to input data uncertainty using this modelling approach. This study presents an innovative stochastic fire simulation decision trigger modelling method that produces a probability map of the fire arrival to areas to be protected by simulating (n) wildfire decision trigger buffers with varied input data according to a potential range of deviations. The Tubbs fire (USA) was used as case study to show the applicability of this approach to estimate the probability of wildland fire impact. Our results highlighted the importance of considering input data uncertainty in operational environments to estimate fire progression and decision trigger buffers to better develop suppression tactic and strategy. The method presented may be solved in real-time and used with any empirical fire propagation model as a core engine. Practical real-time implications of this fire simulation mode are discussed.

Self-organising swarms of firefighting drones: Harnessing the power of collective intelligence in decentralised multi-robot systems

Swarm intelligence (SI) is concerned with the collective behaviour that emerges from decentralised self-organising systems, whilst swarm robotics (SR) is an approach to the self-coordination of large numbers of simple robots which emerged as the application of SI to multi-robot systems. Given the increasing severity and frequency of occurrence of wildfires and the hazardous nature of fighting their propagation, the use of disposable inexpensive robots in place of humans is of special interest. This paper demonstrates the feasibility and potential of employing SR to fight fires autonomously, with a focus on the self-coordination mechanisms for the desired firefighting behaviour to emerge. Thus, an efficient physics-based model of fire propagation and a self-organisation algorithm for swarms of firefighting drones are developed and coupled, with the collaborative behaviour based on a particle swarm algorithm adapted to individuals operating within physical dynamic environments of high severity and frequency of change. Numerical experiments demonstrate that the proposed self-organising system is effective, scalable and fault-tolerant, comprising a promising approach to dealing with the suppression of wildfires – one of the world's most pressing challenges of our time.

Community detection in social networks based on fire propagation

Abstract Community detection is one of the key problem areas in social network analysis. It is classified as an NP-hard problem. Although a lot of good approximate algorithms are available for finding clusters in social networks, all those algorithms have serious drawbacks. They have high time complexities and are inefficient in finding shared communities, a phenomenon common in real-world social networks. In this work a novel community detection algorithm is proposed, that is inspired by the fire propagation model, and is named as Fire Spread community detection algorithm (FSA). When a place catches fire, its nearby places are also at risk of getting burned by it. Getting burned by fire depends on several factors, such as distance from the source of fire, the combustive threshold of the material, number of neighbors already in burning state and heat transmission property of the gap between source and current place. Finding a community near a seed node resembles the phenomena of fire propagation originating from a source. Finally, the proposed algorithm is validated by comparing the modularity and conductance scores with some of the state-of-the-art community detection algorithms for which synthetic and real-world networks are used.

Edge fires drive the shape and stability of tropical forests.

In tropical regions, fires propagate readily in grasslands but typically consume only edges of forest patches. Thus, forest patches grow due to tree propagation and shrink by fires in surrounding grasslands. The interplay between these competing edge effects is unknown, but critical in determining the shape and stability of individual forest patches, as well the landscape-level spatial distribution and stability of forests. We analyze high-resolution remote-sensing data from protected Brazilian Cerrado areas and find that forest shapes obey a robust perimeter-area scaling relation across climatic zones. We explain this scaling by introducing a heterogeneous fire propagation model of tropical forest-grassland ecotones. Deviations from this perimeter-area relation determine the stability of individual forest patches. At a larger scale, our model predicts that the relative rates of tree growth due to propagative expansion and long-distance seed dispersal determine whether collapse of regional-scale tree cover is continuous or discontinuous as fire frequency changes.

Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models

Wildfires are a major concern in Argentinian northwestern Patagonia and in many ecosystems and human societies around the world. We developed an efficient cellular automata model in Graphic Processing Units (GPUs) to simulate fire propagation. The graphical advantages of GPUs were exploited by overlapping wind direction, as well as vegetation, slope, and aspect maps, taking into account relevant landscape characteristics for fire propagation. Stochastic propagation was performed with a probability model that depends on aspect, slope, wind direction and vegetation type. Implementing a Genetic Algorithm search strategy we show, using simulated fires, that we recover the five parameter values that characterize fire propagation. The efficiency of the fire simulation procedure allowed us to also estimate the fire ignition point when it is unknown as well as its associated uncertainty, making this approach suitable for the analysis of fire spread based on maps of burnt areas without knowing the point of origin of the fires or how they spread.

Scalable Cloning on Large-Scale GPU Platforms with Application to Time-Stepped Simulations on Grids

Cloning is a technique to efficiently simulate a tree of multiple what-if scenarios that are unraveled during the course of a base simulation. However, cloned execution is highly challenging to realize on large, distributed memory computing platforms, due to the dynamic nature of the computational load across clones, and due to the complex dependencies spanning the clone tree. We present the conceptual simulation framework, algorithmic foundations, and runtime interface of C lone X, a new system we designed for scalable simulation cloning. It efficiently and dynamically creates whole logical copies of a dynamic tree of simulations across a large parallel system without full physical duplication of computation and memory. The performance of a prototype implementation executed on up to 1,024 graphical processing units of a supercomputing system has been evaluated with three benchmarks—heat diffusion, forest fire, and disease propagation models—delivering a speed up of over two orders of magnitude compared to replicated runs. The results demonstrate a significantly faster and scalable way to execute many what-if scenario ensembles of large simulations via cloning using the C lone X interface.

Emergency logistics for wildfire suppression based on forecasted disaster evolution

This paper aims to develop a two-layer emergency logistics system with a single depot and multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire propagation model is first built using both the flame-igniting attributes of wildfires and the factors affecting wildfire propagation and patterns. Second, based on the forecasted propagation behavior, the emergency levels of fire sites in terms of demand on suppression resources are evaluated and prioritized. For the second layer, considering the prioritized fire sites, the corresponding resource allocation problem and vehicle routing problem (VRP) are investigated and addressed. The former is approached using a model that can minimize the total forest loss (from multiple sites) and suppression costs incurred accordingly. This model is constructed and solved using principles of calculus. To address the latter, a multi-objective VRP model is developed to minimize both the travel time and cost of the resource delivery vehicles. A heuristic algorithm is designed to provide the associated solutions of the VRP model. As a result, this paper provides useful insights into effective wildfire suppression by rationalizing resources regarding different fire propagation rates. The supporting models can also be generalized and tailored to tackle logistics resource optimization issues in dynamic operational environments, particularly those sharing the same feature of single supply and multiple demands in logistics planning and operations (e.g., allocation of ambulances and police forces).

Simulating wildfires backwards in time from the final fire perimeter in point-functional fire models

Fire propagation models simulate wildfires forward in time from a set of ignition locations, but are usually unable to be used backwards if only a final fire perimeter is available. This approach is useful to search fire ignition points, reconstruct past fire events, adjust fire simulators and other purposes. This study proposes three different algorithms: a short time range backwards in time simulation from the perimeter, a statistical analysis related to the likelihood of a fire ignition location over the domain, and an analysis aiming to multiple ignition locations. The methods presented are fast to be solved and may be used with any empirical fire propagation model as a core engine as long as the ROS is locally defined and the model is not coupled to the weather.

Effect of Slope and Packing Ratio on the Behavior of Matchsticks Burnings

The experiment was conducted to demonstrate the behavior of fire propagation in wildlands using a matchsticks forest model. A model forest was designed on a flame resistant clay, on top of which matchsticks were inserted and kept vertical to the ground by keeping space between them constant with the help of aluminum grid. The data for distance travelled by fire with time were taken at wide range of slopes from downhill of ﹣25° to uphill of 45° on a model forest of packing ratios 0.08 and 0.04. The minimum rate of fire spread was observed around 15° downhill. The data collected from this experiment follow tan2θ and agree with the Rothermel’s mathematical model of fire propagation except at elevation above 35° for low packing ratio.

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Significant computation challenges are emerging as agent-based modeling becomes more complicated and dynamically data-driven. In this context, parallel simulation is an attractive solution when dealing with massive data and computation requirements. Nearly all the available distributed simulation systems, however, do not support geospatial phenomena modeling, dynamic data injection, and real-time visualization. To tackle these problems, we propose a distributed dynamic-data driven simulation and analysis system (4D-SAS) specifically for massive spatial agent-based modeling to support real-time representation and analysis of geospatial phenomena. To accomplish large-scale geospatial problem-solving, the 4D-SAS system was spatially enabled to support geospatial model development and employs high-performance computing to improve simulation performance. It can automatically decompose simulation tasks and distribute them among computing nodes following two common schemes: order division or spatial decomposition. Moreover, it provides streaming channels and a storage database to incorporate dynamic data into simulation models; updating agent context in real-time. A new online visualization module was developed based on a GIS mapping library, SharpMap, for an animated display of model execution to help clients understand the model outputs efficiently. To evaluate the system’s efficiency and scalability, two different spatially explicitly agent-based models, an en-route choice model, and a forest fire propagation model, were created on 4D-SAS. Simulation results illustrate that 4D-SAS provides an efficient platform for dynamic data-driven geospatial modeling, e.g., both discrete multi-agent simulation and grid-based cellular automata, demonstrating efficient support for massive parallel simulation. The parallel efficiency of the two models is above 0.7 and remains nearly stable in our experiments.

Curvature effects in the dynamic propagation of wildfires

The behaviour and spread of a wildfire are driven by a range of processes including convection, radiation and the transport of burning material. The combination of these processes and their interactions with environmental conditions govern the evolution of a fire’s perimeter, which can include dynamic variation in the shape and the rate of spread of the fire. It is difficult to fully parametrise the complex interactions between these processes in order to predict a fire’s behaviour. We investigate whether the local curvature of a fire perimeter, defined as the interface between burnt and unburnt regions, can be used to model the dynamic evolution of a wildfire’s progression. We find that incorporation of curvature dependence in an empirical fire propagation model provides closer agreement with the observed evolution of field-based experimental fires than without curvature dependence. The local curvature parameter may represent compounded radiation and convective effects near the flame zone of a fire. Our findings provide a means to incorporate these effects in a computationally efficient way and may lead to improved prediction capability for empirical models of rate of spread and other fire behaviour characteristics.

A Luenberger observer for reaction–diffusion models with front position data

We propose a Luenberger observer for reaction–diffusion models with propagating front features, and for data associated with the location of the front over time. Such models are considered in various application fields, such as electrophysiology, wild-land fire propagation and tumor growth modeling. Drawing our inspiration from image processing methods, we start by proposing an observer for the eikonal-curvature equation that can be derived from the reaction–diffusion model by an asymptotic expansion. We then carry over this observer to the underlying reaction–diffusion equation by an “inverse asymptotic analysis”, and we show that the associated correction in the dynamics has a stabilizing effect for the linearized estimation error. We also discuss the extension to joint state-parameter estimation by using the earlier-proposed ROUKF strategy. We then illustrate and assess our proposed observer method with test problems pertaining to electrophysiology modeling, including with a realistic model of cardiac atria. Our numerical trials show that state estimation is directly very effective with the proposed Luenberger observer, while specific strategies are needed to accurately perform parameter estimation – as is usual with Kalman filtering used in a nonlinear setting – and we demonstrate two such successful strategies.

Size of wildfires in the Euro-Mediterranean region: observations and theoretical analysis

Abstract. MODIS (Moderate Resolution Imaging Spectroradiometer) satellite observations of fire size and ERA-Interim meteorological reanalysis are used to derive a relationship between burnt area and wind speed over the Mediterranean region and Eastern Europe. The largest wildfire size does not show a strong response with respect to wind speed in Eastern Europe. In the Mediterranean, as intuitively expected, the burnt area associated with the largest wildfires is an increasing function of wind speed for moderate temperature anomalies. In situations of severe heatwaves, the relationship between burnt area and wind speed displays a bimodal shape. Burnt areas are large for low 10 m wind speed (lower than 2 m s−1), decrease for moderate wind speed values (lower than 5 m s−1 and larger than 2 m s−1) and increase again for high wind speed (higher than 5 m s−1). To explain such behavior we use a stochastic model of fire propagation, known as a probabilistic cellular automata. This model uses a probabilistic local rule to derive the total burnt area. The observed relationship between burnt area and wind speed can be interpreted in terms of percolation threshold above which the propagation in the model is infinite, which mainly depends on local terrain slope and vegetation state (type, density, fuel moisture). In Eastern Europe, the percolation threshold is never exceeded for observed wind speeds. In the Mediterranean Basin we see two behaviors. During moderately hot weather, the percolation threshold is passed when the wind grows strong. On the other hand, in situations of severe Mediterranean heatwaves, moderate wind speed values impair the propagation of the wildfire against the wind and do not sufficiently accelerate the forward propagation to allow a growth of wildfire size.

Adapting Map Resolution to Accomplish Execution Time Constraints in Wind Field Calculation

Abstract Forest fire propagation prediction is a key point to fight against such hazards. Several models and simulators have been developed to predict forest fire propagation. These models require input parameters such as digital elevation map, vegetation map, and other parameters describing the vegetation and meteorological conditions. Coupling wind field model and forest fire propagation model improves accuracy prediction, but increases significantly prediction time. This fact is critical since propagation prediction must be provided in advance to allow the control centers to manage firefighters in the best possible way. This work analyses WindNinja execution time, describes a WindNinja parallelisation based on map partitioning, determines the limitations of such methodology for large maps and presents an improvement based on adapting map resolution to accomplish execution time limitations.

Thermal behavior of solid particles at ignition: Theoretical limit between thermally thick and thin solids

This paper deals with thermal behaviors of solid particles at ignition in attempting to theoretically delineate transition between thermally thick and thin behavior when a solid target is exposed to a radiant heat flux. In order to evaluate classical asymptotic relation accuracy and limiting range, models are developed for finite-depth target in both Cartesian and cylindrical coordinates, allowing to enhance asymptotic relations. Comparison between finite-depth target solutions and asymptotic solutions finally provides a mapping which allows the suited relation for ignition time calculation to be determined, regarding ignition conditions. This mapping then suggests some interesting consequences on forest fuel ignition and fire propagation modeling, since asymptotic models seem to overlap on large regions.

Evaluation of forest fire models on a large observation database

Abstract. This paper presents the evaluation of several fire propagation models using a large set of observed fires. The observation base is composed of 80 Mediterranean fire cases of different sizes, which come with the limited information available in an operational context (burned surface and approximative ignition point). Simulations for all cases are carried out with four different front velocity models. The results are compared with several error scoring methods applied to each of the 320 simulations. All tasks are performed in a fully automated manner, with simulations run as first guesses with no tuning for any of the models or cases. This approach leads to a wide range of simulation performance, including some of the bad simulation results to be expected in an operational context. Disregarding the quality of the input data, it is found that the models can be ranked based on their performance and that the most complex models outperform the more empirical ones. Data and source codes used for this paper are freely available to the community.

Enhancing multi-model forest fire spread prediction by exploiting multi-core parallelism

The Two-Stage forest fire spread prediction methodology was developed to enhance forest fire evolution forecast by tackling the uncertainty of some environmental conditions. However, there are parameters, such as wind, that present a variation along terrain and time. In such cases, it is necessary to couple forest fire propagation models and complementary models, such as meteorological forecast and wind field models. This multi-model approach improves the accuracy of the predictions by introducing an overhead in the execution time. In this paper, different multi-model approaches are discussed and the results show that the propagation prediction is improved. Exploiting multi-core architectures of current processors, we can reduce the overhead introduced by complementary models.

GENETIC ALGORITHM BASED SIMULATION–OPTIMIZATION FOR FIGHTING WILDFIRES

Wildfire is one of the most significant disturbances responsible for reshaping the terrain and changing the ecosystem of a particular region. Its detrimental effects on environment as well as human lives and properties, and growing trend in terms of frequency and intensity of wildfires over the last decade have necessitated the development of efficient forest fire management techniques. During the last three decades, Forest Fire Decision Support Systems (FFDSS) have been developed to help in the decision-making processes during forest fires by providing necessary information on fire detection, their status and behavior, and other aspects of forest fires. However, most of these decision support systems lack the capability of developing intelligent fire suppression strategies based upon current status and predicted behavior of forest fire. This paper presents an approach for development of efficient fireline building strategies via intelligent resource allocation. A Genetic Algorithm based approach has been proposed in this paper for resource allocation and optimum fireline building that minimizes the total damage due to wildland fires. The approach is based on a simulation–optimization technique in which the Genetic Algorithm uses advanced forest fire propagation models based upon Huygens principles for evaluation of cost index of its solutions. Both homogeneous and heterogeneous environmental conditions have been considered. Uncertainties in weather conditions as well as imperfect knowledge about exact vegetation and topographical conditions make exact prediction of wildfires very difficult. The paper incorporates Monte-Carlo simulations to develop robust strategies in uncertain conditions. Extensive simulations demonstrate the effectiveness of the proposed approach in efficient resource allocation for fighting complex wildfires in uncertain and dynamic conditions.

Fuel model development for the Greek East‐Mediterranean forest litter layer

SUMMARYIn order to develop fuel models used as input in wildland fire propagation models, it is necessary to determine relevant vegetation properties concerned with the prediction of the combustion process. This research considers property measurements of two biomass species, namely Pinus halepensis and Pinus brutia. Both species are commonly found in Greece and other Eastern Mediterranean countries. The physical application of the biomass properties relates to the structure of pine forest litter. Measurements have been recorded of the characteristics of the litter layer in situ, and common fuel properties have been determined in the laboratory using samples of the two litter species. In addition, DTA, TG and DTG analysis were performed of the two litter species. The results of the measurements are presented in a format suitable for input in fire propagation models. Comparisons of data have been made with those from other literature sources. Copyright © 2012 John Wiley & Sons, Ltd.