PROPAGATOR: An Operational Cellular-Automata Based Wildfire Simulator

Trucchia, Andrea; D’Andrea, Mirko; Baghino, Francesco; Fiorucci, Paolo; Ferraris, Luca; Negro, Dario; Gollini, Andrea; Severino, Massimiliano

doi:10.3390/fire3030026

Cited by 40 publications

(30 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simulation results have been compared with those obtained from the CIMA Propagator [69], an experimental propagation model that has provided several European civil protection organizations with real-time fire predictions since 2009. The model has been implemented by the CIMA Foundation, and its propagation model is based on stochastic cellular automata.…”

Section: Complex Terrain Fire Spreadmentioning

confidence: 99%

A Multi-Fidelity Framework for Wildland Fire Behavior Simulations over Complex Terrain

et al. 2021

Self Cite

View full text Add to dashboard Cite

A method for the large-eddy simulation (LES) of wildfire spread over complex terrain is presented. In this scheme, a cut-cell immersed boundary method (CC-IBM) is used to render the complex terrain, defined by a tessellation, on a rectilinear Cartesian grid. Discretization of scalar transport equations for chemical species is done via a finite volume scheme on cut-cells defined by the intersection of the terrain geometry and the Cartesian cells. Momentum transport and heat transfer close to the immersed terrain are handled using dynamic wall models and a direct forcing immersed boundary method. A new “open” convective inflow/outflow method for specifying atmospheric wind boundary conditions is presented. Additionally, three basic approaches have been explored to model fire spread: (1) Representing the vegetation as a collection of Lagrangian particles, (2) representing the vegetation as a semi-porous boundary, and (3) representing the fire spread using a level set method, in which the fire spreads as a function of terrain slope, vegetation type, and wind speed. Several test and validation cases are reported to demonstrate the capabilities of this novel wildfire simulation methodology.

show abstract

Section: Complex Terrain Fire Spreadmentioning

confidence: 99%

A Multi-Fidelity Framework for Wildland Fire Behavior Simulations over Complex Terrain

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…CA [16][17][18][19] has been applied in many disciplines because it is easy to use on computers [20]. CA [21,22] has better simulated performance in complex forest environments because fires do not usually spread in elliptical patterns. CA has been used by many scholars to simulate the spread of forest fires.…”

Section: Introductionmentioning

confidence: 99%

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Zhang

et al. 2022

Fire

View full text Add to dashboard Cite

The simulation of forest fire spread is a key problem for the management of fire, and Cellular Automata (CA) has been used to simulate the complex mechanism of the fire spread for a long time. The simulation of CA is driven by the rate of fire spread (ROS), which is hard to estimate, because some input parameters of the current ROS model cannot be provided with a high precision, so the CA approach has not been well applied yet in the forest fire management system to date. The forest fire spread simulation model LSTM-CA using CA with LSTM is proposed in this paper. Based on the interaction between wind and fire, S-LSTM is proposed, which takes full advantage of the time dependency of the ROS. The ROS estimated by the S-LSTM is satisfactory, even though the input parameters are not perfect. Fifteen kinds of ROS models with the same structure are trained for different cases of slope direction and wind direction, and the model with the closest case is selected to drive the transmission between the adjacent cells. In order to simulate the actual spread of forest fire, the LSTM-based models are trained based on the data captured, and three correction rules are added to the CA model. Finally, the prediction accuracy of forest fire spread is verified though the KAPPA coefficient, Hausdorff distance, and horizontal comparison experiments based on remote sensing images of wildfires. The LSTM-CA model has good practicality in simulating the spread of forest fires.

show abstract

“…These can be classified into physics-based methods, multi-criteria analyses coupled with statistical approaches, and machine learning methods. Physics-based methods involve the simulation and the prediction of fire behaviors through mathematical equations of fluid mechanics, combustion of canopy biomass, and heat transfer mechanisms [11][12][13][14][15]. Multi-criteria analyses, coupled with statistical methods, assumes that the probability of occurrence of burned areas can be quantitatively assessed by investigating the relationship between fire occurrences and predisposing factors [16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Spatial Assessment of Wildfires Susceptibility in Santa Cruz (Bolivia) Using Random Forest

et al. 2021

Self Cite

View full text Add to dashboard Cite

Wildfires are expected to increase in the near future, mainly because of climate changes and land use management. One of the most vulnerable areas in the world is the forest in central-South America, including Bolivia. Despite that this country is highly prone to wildfires, literature is rather limited here. To fill this gap, we implemented a dataset including the burned area that occurred in the department of Santa Cruz in the period of 2010–2019, and the digital spatial data describing the predisposing factors (i.e., topography, land cover, ecoregions). The main goal was to develop a model, based on Random Forest, in which probabilistic outputs allowed to elaborate wildfires susceptibility maps. The overall accuracy was finally estimated by using 5-fold cross-validation. In addition, the last three years of observations acted as the testing dataset, allowing to evaluate the predictive performance of the model. The quantitative assessment of the variables revealed that “flooded savanna” and “shrub or herbaceous cover, flooded, fresh/saline/brakish water” are respectively the ecoregions and land cover classes with the highest probability of predicting wildfires. This study contributes to the development and validation of an innovative mapping tool for fire risk assessment, implementable at a regional scale in different areas of the globe.

show abstract

PROPAGATOR: An Operational Cellular-Automata Based Wildfire Simulator

Cited by 40 publications

References 42 publications

A Multi-Fidelity Framework for Wildland Fire Behavior Simulations over Complex Terrain

A Multi-Fidelity Framework for Wildland Fire Behavior Simulations over Complex Terrain

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Spatial Assessment of Wildfires Susceptibility in Santa Cruz (Bolivia) Using Random Forest

Contact Info

Product

Resources

About