Cellular Automaton based Fire Spreading Simulation in Closed Area: Clogging Region Detection

Assessing and reinitializing wildland fire simulations through satellite active fire data

The aim of this paper is to propose a more practical mountain fire spread model for fire behavior prediction and management in Southwest forest area of China. These areas are covered mainly with spatial heterogeneous flammable forest and are characterized by undulating terrain and steep slopes. This model can produce more accurate fire propagation maps by combining CA (Cellular Automaton) framework with Wang Zhengfei fire physical velocity model in fine scale. Considering the inherent uncertainties of fire propagation, the model has been built on multi-dimension geophysical and environmental components and also sound knowledge of fire spread physical mechanism. Regarding small fuel patches as spatial homogenous cells, this approach makes it easier to generate higher level complex fire behavior maps from CA simple local rules and local behavior integrated with high resolution vegetation images, fine scale terrain maps and surface wind field. Because the model focuses primarily on the study of surface fire front propagation behavior, it attempts to simplify complex fuel modeling. Additionally, this Wang-Geophysical-CA model is able to analyze the time series spatial pattern of fire-front spread and model local behavior instead of the final fire spread pattern of the conventional approach. In this work, not only single influence verification tests have been made, but also simulation tests with multiple influences are carried out to demonstrate the capability of the model with fine scale vegetation maps, surface wind field, terrain, moisture content and man-made structures. Consequently, it is believable that the model predictions are in good agreement with experimental data for steady-state fire simulation. The proposed model helps to gain a greater understanding of the fire front spread local behavior and can quickly generate a sequence of complex fire front contours. It enables local managers to plan practical fire prevention activities in Southwest forest area of China as well as improve fire management skills, and will enhance the effectiveness of fire fighting strategies.

Abstract. A fire simulator and an evacuation simulator are generally used independently to diagnose the safety of a building in the case of the fire and evacuation. However, it is hard to provide highly accurate safety diagnosis with this method because it does not reflect the movement of pedestrians in the situation of a fire. Therefore, this study proposed a fire evacuation simulation technique that can describe the movement of pedestrians with considering the fire spread. The proposed simulation technique applies the fire spread data of the fire dynamics simulator (FDS) to the floor field model (FFM) and it models that pedestrians recognizes the fire and take a detour to a safe route. This study proposed a method to link the data between FDS and FFM and an improved FFM considering fire spread. Additionally, the proposed method was applied to a real building on a university campus. This study simulated evacuations under various scenarios. Simulation results showed that the number of evacuees escaping through each exit varied by the presence of fire. Moreover, it was found that the evacuation time was increased or decreased by the fire and bottleneck phenomenon was also worsened under fire situation.

Wildfires are an increasing threat in Mediterranean regions, where extreme fire weather and long-term fuel accumulation are driving more frequent and severe events. In this context, fast and reliable fire spread simulations are essential to support both risk mitigation planning and real-time emergency management. PROPAGATOR is a stochastic Cellular Automata (CA) wildfire spread simulator designed to generate ensemble-based fire spread forecasts. The model, currently available as both an online application and open-source software, operates within a raster-based framework in which each cell is described by static attributes (e.g. fuel type, topography) and dynamic drivers (e.g. wind, fuel moisture). Fire propagation is modelled through a stochastic contamination process between burning and unburned cells, allowing the production of probabilistic maps of fire spread, as well as statistics on rate of spread and fireline intensity. PROPAGATOR also includes the capability to simulate the spotting phenomenon and suppression actions such as water drops or firebreak construction, making it suitable for both operational decision support during active fires and pre-event risk mitigation analyses. A current limitation of operational applications of PROPAGATOR is its focus on surface fire propagation, with no explicit representation of vertical fuel structure or transitions to crown fire. Crown fires, however, are characterized by higher spread rates, greater energy release, and increased unpredictability, with major implications for suppression effectiveness and ecological impacts. To address this limitation, an enhanced version of PROPAGATOR has been developed by extending the model to a quasi-three-dimensional (2.5D) representation of fuels, enabling the simulation of crown fire processes within the stochastic CA framework. The proposed Crown Fire Module relies on established empirical and semi-empirical formulations for crown fire initiation and spread that are compatible with a cellular automata approach. Crown fire initiation is governed by surface fireline intensity and crown base height, while crown fire rate of spread depends primarily on canopy bulk density and fire behaviour. These mechanisms have been integrated into the propagation rules of PROPAGATOR, allowing dynamic transitions between surface and crown fire behaviour within a probabilistic modelling framework. The implementation of these processes requires detailed information on both surface and canopy fuel structure and characteristics, which remains challenging at operational scales. To address this issue, we investigated the use of UAV-based LiDAR remote sensing to derive key fuel structure parameters using semi-automatic algorithms available in the literature. This approach offers a balance between spatial detail and areal coverage that is suitable for operational wildfire applications. A pilot study conducted in the Venafro area (Molise, Italy), based on a past wildfire event with a comprehensive dataset describing fire evolution, provided high-resolution inputs to test the enhanced model. By explicitly simulating surface-to-crown fire transitions, the upgraded version of PROPAGATOR aims to improve decision support for wildfire risk management, supporting applications ranging from fuel treatment planning to operational response under extreme fire weather conditions.

Wildland fire simulation is a useful tool for studying wildland fires and for developing new technologies to help wildland fire management. This paper presents an effort of developing wildland fire simulation as a service that is accessible to broader users. The developed simulation service supports fire spread simulation, fire suppression simulation, and prescribed fire simulation with dynamic ignitions, and is accessible through a graphic user interface and an application programming interface (API). We present the underlying DEVS-FIRE simulation model, the design of the cloud-based wildland fire simulation service, and some preliminary results.

With the acceleration of urban construction and power grid transformation, underground cables play an increasingly important role in urban power supply. However, in recent years, frequent cable fire accidents have seriously affected the safe operation of power grid. The importance of cable fire prevention, especially cable tunnel fire prevention, is increasing day by day. Practice has proved that the use of cable fire barrier can effectively inhibit the spread of cable fire and reduce the risk of cable combustion. However, the problem of which is the best way to lay the fire barrier has not been solved. Based on the finite element simulation software, this paper establishes a fire simulation model of underground high-voltage cable trench in substation. By simulating the fire spread trend, the change of temperature field inside the tunnel and the difference of various gas distribution under the two common laying methods of fireproof partition, it draws the following conclusions: the laying method of fireproof partition above the cable is better than the laying method of fireproof partition below the cable, Compared with the cable laying method of the lower fireproof partition, the laying method of the upper fireproof partition can more effectively slow down the fire spreading speed, produce less toxic gas, and form a lower fire temperature field.

This paper presents a first effort to integrate DEVS-based cellular space models with agent models using dynamic structure DEVS for the simulation of forest fire spread and suppression. The main focus is on the interaction between mobile agents (such as fire fighters, air-tankers) and forest cells. DEVS models' dynamic structure modeling capability is applied where couplings between mobile agents and forest cells are dynamically added and removed when the agents move in the cellular space. Two methods, a time-based method and an event-based method, are discussed to update a mobile agent's position in a cellular space. A system architecture is presented and a prototype fire spread and suppression example is implemented. Based on this initial work, we hope to understand more about the nature of this hybrid agent-cellular space approach and to apply it to the modeling and simulation of forest fires and other ecological applications.

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) Forestry and Forest Products has developed a PC-based fire spread prediction application that can be used by fire control officers as an aid in strategic planning and resource allocation. This computer application, called Siro Fire, is a DOS protected mode application that incorporates several fire spread models that can be applied to the two major fuel types found in Australia-grass and forest. It uses GIS-derived geographic maps and digital elevation models to calculate the probable spread of a fire across the landscape and displays the results on a graphical representation of the map of the area of concern. The application is being used in the South Australian Country Fire Service as both an operational and training tool. This paper outlines the evolution of previous fire spread simulation software, the development of SiroFire and how SiroFire carries out fire spread simulations using existing fire spread prediction algorithms.

Wildfires are a common and devastating event that impacts ecosystems and communities yearly. Fire experts rely on simulations to better understand how to mitigate the damage they cause and respond to live threats. Many available fire simulation tools and libraries do not produce simulation results fast enough to be used with multiple parameter sets during an active fire and are difficult to integrate with other applications. To address this gap, we propose a GPU-based fire simulation and visualization tool: vFirelib. Using a GPGPU (General Purpose Graphics Processing Unit) framework, we can parallelize the fire spread computations and achieve a 20X computation speedup over a sequential implementation of a fire spread using a widely used fire spread model. To facilitate the integration with other applications, we implemented a wrapper including RESTful APIs to provide fire simulation as a service. In this paper, two examples are illustrated how to simulate wildfire scenarios and visualize results: a web-based application, and a 3D virtual reality application.

Taking the JINSHANZHAI highway tunnel in Xiamen as an example, people safety evacuation in urban highway tunnel fire accidents was conducted in this paper. According to the most unfavorable fire scenario for safety evacuation, the AEST can be obtained through fire Spread and smoke diffusion simulation. Then the REST of different staff loads were calculated using empirical formula. Through comparing of AEST and REST, whether evacuation passage setting can meet the evacuation requirements or not was confirmed, and then solutions were proposed.

Accurate wildfire spread modeling critically depends on the representation of wind dynamics, which vary with terrain, land cover characteristics, and height above ground. Many fire spread models are often coupled with coarse atmospheric grids that cannot explicitly resolve the vertical variation of wind near flame heights. Rothermel’s fire spread model, a widely used parameterization, relies on midflame wind speed to calculate the fire rate of spread. In coupled fire atmosphere models such as the Community Fire Behavior Model (CFBM), users are required to specify the midflame height before running a fire spread simulation. This study evaluates the use of logarithmic interpolation wind adjustment factors (WAF) for improving midflame wind speed estimates, which are critical for the Rothermel model. We compare the fixed wind height approach that is currently used in CFBM with WAF-derived winds for unsheltered and sheltered surface fire spread. For the first time in this context, these simulations are validated against satellite and ground-based observations of fire perimeters. The results show that WAF implementation improves fire perimeter predictions for both grass and canopy fires while reducing the overestimation of fire spread. Moreover, this approach solely depends on the fuel bed depth and estimation of canopy density, enhancing operational efficiency by eliminating the need for users to specify a wind height for simulations.

The use of fire simulation tools has become a regular feature of support systems for fuel management decisions at landscape level. Considering the spatial nature of fire in the evaluation of risk and the definition of fire mitigation goals is an ongoing research topic in forest management planning. By combining a fire simulation tool, a growth and yield simulator and an optimization module, it is possible to minimize the negative impact of fire over time and maximize the yield of various ecosystem services. Specific requirements for a fire simulator adapted to support tactical forest planning include a level of accuracy, the possibility of exploring diverse fire scenarios, the computational capability to simulate multiple fires and the flexibility to generate different outputs or metrics depending on the specific requirements of the planning problem under study. The present article addresses the requirements of fire simulators for their inclusion on forest tactical planning. The Cell2Fire simulator is adapted for use with fuel models more commonly employed in Europe and the United States, and to simulate the generation and spread of crown fires. Already able to solve static fire mitigation problems in its original version, this new adaptation, known as Cell2Fire_SB, has been developed with the more ambitious goal of being integrated into a decision support system that simultaneously considers fire behavior forest dynamics and allocation of management actions in order to solve temporal dynamic tactical forest problems.

An improved cellular automaton model for simulating fire in a spatially heterogeneous Savanna system

Climate change causes more extreme droughts and heat waves in Central Europe, affecting vegetative fuels and altering the local fire regime. Wildfire is projected to expand into the temperate zone, a region traditionally not concerned by fire. To mitigate this new threat, local forest management will require spatial fire hazard information. We present a holistic and comprehensible workflow for quantifying fuels and wildfire hazard through fire spread simulations. Surface and canopy fuels characteristics were sampled in a small managed temperate forest in Northern Germany. Custom fuel models were created for each dominant species (Pinus sylvestris, Fagus sylvatica, and Quercus rubra). Canopy cover, canopy height, and crown base height were directly derived from airborne LiDAR point clouds. Surface fuel types and crown bulk density (CBD) were predicted using random forest and ridge regression, respectively. Modeling was supported by 119 predictors extracted from LiDAR, Sentinel-1, and Sentinel-2 data. We simulated fire spread from random ignitions, considering eight environmental scenarios to calculate fire behavior and hazard. Fuel type classification scored an overall accuracy of 0.971 (Kappa = 0.967), whereas CBD regression performed notably weaker (RMSE = 0.069; R2 = 0.73). Higher fire hazard was identified for strong winds, low fuel moisture, and on slopes. Fires burned fastest and most frequently on slopes in large homogeneous pine stands. These should be the focus of preventive management actions.

Wildfire risks are projected to increase in the future due to climate change, coupled with increased exposure along the wildland-urban interface (WUI) due to population growth and growing cities.This research presents a modelling framework for simulating potential risks posed by wildland fires to urban areas across the WUI via a multi-stage, loosely coupled approach:Stage 1 - Wildfire: Using Spark, user-defined equations determine fire spread behaviour (Miller et al. 2015). To calculate Rate of Spread (RoS) and Fireline intensities for New Zealand vegetation types, equations from Pearce (2005) are applied. These, along with localised climate data for current and future conditions, create wildfire scenarios. Flame heights and radiant heat flux (RHF) are spatially analysed at each time-step to assess risks to transport networks, critical infrastructure, and buildings near or outside urban areas along the WUI.Stage 2 – Building-to-building fire: With ignition points at urban boundaries defined, the second stage of the modelling framework uses a physics-based building-to-building fire spread model like that outlined in Himoto (2022) to simulate urban fire propagation over time. This, combined with wildfire model outputs, informs the risk assessment and micro-scale evacuation model.Stage 3 – Ensemble risk assessment:  Fire exposure from previous stages is used to assess risks to infrastructure and transportation networks. A method, adapted from Butler and Cohen (1998), defines RHF values and maps it to the transport network. This data informs the evacuation model, defining safe zones and low-risk evacuation corridors.Stage 4 – Evacuation modelling:  Building on work by Evans et al. (2020), the evacuation model integrates hazard outputs with micro-scale transport models to simulate evacuee movement under extreme scenarios. By incorporating movement restrictions and evacuee behaviours, it assesses risks to evacuees navigating the network.Together, these four stages provide a comprehensive risk assessment of wildfires and key insights for refining evacuation planning strategies.AcknowledgementThis project has received funding from the European Union's Horizon Europe research and innovation programme under the grant agreement number 101147385. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or CINEA. Neither the European Union nor the granting authority can be held responsible for them.ReferencesButler, W, B., Cohen, D, J. (1998). Firefighter Safety Zones: A Theoretical Model Based on Radiative Heating International Journal of Wildland Fire 8(2) 73 – 77. https://doi.org/10.1071/WF9980073Evans, B., Chen, A.S., Djordjevic, S., Webber, J., Gómez, A.G., and Stevens, J. (2020). Investigating the 421 effects of pluvial flooding and climate change on traffic flows in Barcelona and Bristol. Sustainability, 422 12(6), 2330. https://doi.org/10.3390/su12062330Himoto, K. (2022). Large Outdoor Fire Dynamics – Fire Spread Simulation (pp 333 – 367). 1st Edition. CRC Press. http://dx.doi.org/10.1201/9781003096689-10Miller, C., Hilton, J., Sullivan, A., Prakash, M. (2015). SPARK – A Bushfire Spread Prediction Tool. In: Denzer, R., Argent, R.M., Schimak, G., Hřebíček, J. (eds) Environmental Software Systems. Infrastructures, Services and Applications. ISESS 2015. IFIP Advances in Information and Communication Technology, vol 448. Springer, Cham. https://doi.org/10.1007/978-3-319-15994-2_26Pearce, G. H. (2025). Appendix 3: Sub-contracted Report: Fuel Load and Fire Behaviour Assessments for Vegetation within LCDB2