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

Brun, Carlos; Margalef, Tomás; Cortés, Ana; Sikora, Anna

doi:10.1007/s11227-014-1168-z

Cited by 18 publications

(4 citation statements)

References 9 publications

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“…The design formulas (12) have been obtained for the case of steady motion of soil throwing machine, which determine the values of the traction force from the tractor and the torque which ensures the operation of the milling tool.…”

Section: Resultsmentioning

confidence: 99%

Mathematical Model of Forest Fire Soil-thrower Movement

Dornyak

Бартенев

Drapalyuk

et al. 2020

KLS

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The design of a forest fire soil-thrower made to prevent and eliminate ground forest fires is presented. A mathematical model of machine movement has been developed, which enables to study the laws of the interaction process of the design with the soil. It is accepted that the machine has two degrees of freedom. The mathematical model has been obtained using the Lagrange equations of the second kind. The design and technological parameters of the forest fire soil-throwing machine, affecting the efficiency of its work, including mass and width of the grip of the ripper casing, mass, radius and frequency of rotation of the milling tool, the number and geometric parameters of the blades are taken into account. Mathematical model enables to determine the effect of these parameters on the characteristics of the movement of ripper casing and milling working body. A mathematical model is needed to synchronize the translational motion of the unit and the rotational motion of the rotor. Formulas have been obtained for the steady motion of the forest fire soil-thrower, that determine the hauling power of tractor and torque that ensures the operation of milling tools.

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Section: Resultsmentioning

confidence: 99%

Mathematical Model of Forest Fire Soil-thrower Movement

Dornyak

Бартенев

Drapalyuk

et al. 2020

KLS

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“…2 and Formula (2), the burning state of the central cell at the t+Δt is determined by the state of the domain cell at the time t and the spreading speed of the domain cell, where R is the forest fire spread speed, and its value is the Rothermel forest fire speed formula, which will be discussed in the section of "The simplified Rotherme". (Brun et al 2014). Rothermel forest fire spread is known by researchers as a semi-empirical forest fire spread model (Denham et al 2012;Plucinski et al 2013), such as formulas ( 4)-( 11) are Rothermel speed formula, where I R is the reaction intensity, ξ is the spread rate, Ф W is the wind speed correction coefficient, Ф S is the slope correction coefficient, P b is the…”

Section: The State Of Md-camentioning

confidence: 99%

Study on Forest Fire spread Model of Multi-dimensional Cellular Automata based on Rothermel Speed Formula

Zhang

Liu

Gao

et al. 2021

CERNE

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Background:The spread of forest fire is a complex natural phenomenon. The cellular automata(CA) is a common model of forest fire spread, which fails to combine the unique combustion properties of forest fire spreading, resulting in inaccurate simulation results. In order to improve the accuracy of forest fire spread simulation, the Rothermel forest fire rate formula is simplified and combined with CA to form a multi-dimensional cellular automata(MD-CA) model of forest fire spread with different combustion properties in each cell. The formulas of the spread rate of forest fire in eight directions are obtained through the training datasets, and the testing datasets are used to compare the simulation results of CA model, MD-CA model and the actual fire spread. Results:It is concluded that the simulated areas and perimeters of the MD-CA are closer to the actual forest fire spread, the area error rate is 9.42% -15.63%, and the perimeter error is 4.21% -8.99%, The errors are less than CA model. Conclusion:The MD-CA model based on Rothermel has the strong ability to simulate the spread of fire in the laboratory, however, how to eliminate the errors over time is the task of the next stage.

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“…It has been proven by some other researches such as Brun et al [37] and Podschwit et al [38] that ensemble and multimodel approaches might lead to much more accurate results. Zhou [39] stated that ensemble modeling offers a state-ofthe-art learning approach, which has become a focus of modeling research since the 1990s and has been shown to produce results that are considerably more precise than using a single method [40][41][42][43].…”

Section: Introductionmentioning

confidence: 98%

Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping

et al. 2021

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The survival of humanity is dependent on the survival of forests and the ecosystems they support, yet annually wildfires destroy millions of hectares of global forestry. Wildfires take place under specific conditions and in certain regions, which can be studied through appropriate techniques. A variety of statistical modeling methods have been assessed by researchers; however, ensemble modeling of wildfire susceptibility has not been undertaken. We hypothesize that ensemble modeling of wildfire susceptibility is better than a single modeling technique. This study models the occurrence of wildfire in the Brisbane Catchment of Australia, which is an annual event, using the index of entropy (IoE), evidential belief function (EBF), and logistic regression (LR) ensemble techniques. As a secondary goal of this research, the spatial distribution of the wildfire risk from different aspects such as urbanization and ecosystem was evaluated. The highest accuracy (88.51%) was achieved using the ensemble EBF and LR model. The outcomes of this study may be helpful to particular groups such as planners to avoid susceptible and risky regions in their planning; model builders to replace the traditional individual methods with ensemble algorithms; and geospatial users to enhance their knowledge of geographic information system (GIS) applications.

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

Cited by 18 publications

References 9 publications

Mathematical Model of Forest Fire Soil-thrower Movement

Mathematical Model of Forest Fire Soil-thrower Movement

Study on Forest Fire spread Model of Multi-dimensional Cellular Automata based on Rothermel Speed Formula

Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping

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