Visualization and modeling of forest fire propagation in Patagonia

Denham, Mónica Malén; Waidelich, Sigfrido; Laneri, Karina

doi:10.1016/j.envsoft.2022.105526

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…[33]. The latter models and the stochastic one will contain the same parameters, which can be found by a comparison with the RDC models 40,41 present in the literature whose parameters have been fitted with real data 40 . Another possibility would be to use optimal control theory to find the parameters that make the simulation results of the retrieved macroscopic models to match with real data.…”

Section: Numerical Simulationsmentioning

confidence: 99%

See 1 more Smart Citation

Forest fire spreading: A nonlinear stochastic model continuous in space and time

Beneduci,

Mascali

2024

Stud Appl Math

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Forest fire spreading is a complex phenomenon characterized by a stochastic behavior. Nowadays, the enormous quantity of georeferenced data and the availability of powerful techniques for their analysis can provide a very careful picture of forest fires opening the way to more realistic models. We propose a stochastic spreading model continuous in space and time that has the potentiality to use such data in their full power. The state of the forest fire is described by the subprobability densities of the green trees and of the trees on fire that can be estimated thanks to data coming from satellites and earth detectors. The fire dynamics is encoded into a kernel function that can take into account wind conditions, land slope, spotting phenomena, and so on, bringing to a system of integrodifferential equations whose solutions provide the evolution in time of the subprobability densities. That makes the model complementary to models based on cellular automata that furnish single instantiations of the stochastic phenomenon. Moreover, stochastic models based on cellular automata can be derived from the present model by space and time discretization. Existence and uniqueness of the solutions is proved by using Banach's fixed‐point theorem. The asymptotic behavior of the model is analyzed as well. By specifying a particular structure for the kernel, we obtain numerical simulations of the fire spreading under different conditions. For example, in the case of a forest fire evolving toward a river, the simulations show that the probability density of the trees on fire is different from zero beyond the river due to the spotting phenomenon. The kernel could be slightly modified to include firefighters interventions and weather changes.

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Section: Numerical Simulationsmentioning

confidence: 99%

“…There exist relevant deterministic models for fire spreading, both phenomenological and physical, both based on differential equations and on cellular automata (CA), [9][10][11][12][13][14][15][16][17] see Sullivan 18 for a review. Some of them are usually combined in simulation softwares.…”

Section: Introductionmentioning

confidence: 99%

Forest fire spreading: A nonlinear stochastic model continuous in space and time

Beneduci,

Mascali

2024

Stud Appl Math

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“…In recent years, with the rapid development of geographic information technology globally, the visualization of wildfire spread has also made significant progress. During this process, numerous simulation software programs have been developed, such as Prometheus in Canada [71], BehavePlus, developed by the United States Forest Service [72], FARSITE [73] and FlmaMap [74], HPC forest fire simulator developed by Argentine scholars [75], Open Source Software Cell2Fire [76] and ForeFire [77]. Therefore, it is inevitable to use appropriate computer system applications for wildfire spread simulation.…”

Section: Wildfire Spread Behavior Simulation Applicationmentioning

confidence: 99%

Facing the Wildfire Spread Risk Challenge: Where Are We Now and Where Are We Going?

Sun

Huang

et al. 2023

Fire

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Wildfire is a sudden and highly destructive natural disaster that poses significant challenges in terms of response and rescue efforts. Influenced by factors such as climate, combustible materials, and ignition sources, wildfires have been increasingly occurring worldwide on an annual basis. In recent years, researchers have shown growing interest in studying wildfires, leading to a substantial body of related research. These studies encompass various topics, including wildfire prediction and forecasting, the analysis of spatial and temporal patterns, the assessment of ecological impacts, the simulation of wildfire behavior, the identification of influencing factors, the development of risk assessment models, techniques for managing combustible materials, decision-making technologies for firefighting, and fire-retardant methods. Understanding the factors that affect wildfire spread behavior, employing simulation methods, and conducting risk assessments are vital for effective wildfire prevention, disaster mitigation, and emergency response. Consequently, it is imperative to comprehensively review and explore further research in this field. This article primarily focuses on elucidating and discussing wildfire spread behavior as a key aspect. It summarizes the driving factors of wildfire spread behavior and introduces a wildfire spread behavior simulation software and its main applications based on these factors. Furthermore, it presents the research progress in wildfire risk assessment based on wildfire spread behavior factors and simulation, and provides an overview of various methods used for wildfire risk assessment. Finally, the article proposes several prospects for future research on wildfire spread: strengthening the dynamic monitoring of wildfires and utilizing comprehensive data from multiple sources, further exploring the differential effects of key factors on wildfire spread, investigating differences in driving factors, improving wildfire models in China, developing applicable software, and conducting accurate and scientific assessments of wildfire risks to protect ecological resources.

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“…At present, a large number of studies have been carried out on forest fire spread [5]. The research methods include the real measurement of fire lines based on planned burning and laboratory simulation combustion; remote sensing via satellites, aircraft, and unmanned aerial vehicles (UAVs); and forest fire spread modeling.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Different Models to Simulate Forest Fire Spread: A Case Study

Ning,

Liu,

et al. 2024

Forests

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With the development of computer technology, forest fire spread simulation using computers has gradually developed. According to the existing research on forest fire spread, the models established in various countries have typical regional characteristics. A fire spread model established in a specific region is only suitable for the local area, and there is still a great deal of uncertainty as to whether or not the established model is suitable for fire spread simulation for the same fuel in other regions. Although many fire spread models have been established, the fuel characteristics applicable to each model, such as the fuel loading, fuel moisture content, combustibility, etc., are not similar. It is necessary to evaluate the applicability of different fuel characteristics to different fire spread models. We combined ground investigation, historical data collection, model improvements, and statistical analysis to establish a multi-model forest fire spread simulation method (FIRER) that shows the burning time, perimeter, burning area, overlap area, and spread rate of fire sites. This method is a large-scale, high-resolution fire growth model based on fire spread in eight directions on a regular 30 m grid. This method could use any one of four different physical models (McArthur, Rothermel, FBP, and Wang Zhengfei (China)) for fire behavior. This method has an option to represent fire breaks from roads, rivers, and fire suppression. We can evaluate which model is more suitable in a specific area. This method was tested on a single historical lightning fire in the Daxing’an Mountains. Different scenarios were tested and compared: using each of the four fire behavior models, with fire breaks on or off, and with a single or suspected double fire ignition location of the historical fire. The results show that the Rothermel model is the best model in the simulation of the Hanma lightning fire; the overlap area is 5694.4 hm2. Meanwhile, the real fire area in FIRER is 5800.9 hm2; both the Kappa and Sørensen values exceed 0.8, providing high accuracy in fire spread simulations. FIRER performs well in the automatic identification of fire break zones and multiple ignited points. Compared with FARSITE, FIRER performs well in predicting accuracy. Compared with BehavePlus, FIRER also has advantages in simulating large-scale fire spread. However, the complex data preparation stage of FIRER means that FIRER still has great room for improvement. This research provides a practical basis for the comparison of the practicability and applicability of various fire spread models and provides more effective practical tools and a scientific basis for decision-making and the management of fighting forest fires.

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Visualization and modeling of forest fire propagation in Patagonia

Cited by 8 publications

References 17 publications

Forest fire spreading: A nonlinear stochastic model continuous in space and time

Forest fire spreading: A nonlinear stochastic model continuous in space and time

Facing the Wildfire Spread Risk Challenge: Where Are We Now and Where Are We Going?

Comparison of Different Models to Simulate Forest Fire Spread: A Case Study

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