Parameter estimation of fire propagation models using level set methods

Alessandri, A.; Bagnerini, Patrizia; Gaggero, Mauro; Mantelli, Luca

doi:10.1016/j.apm.2020.11.030

Cited by 28 publications

(15 citation statements)

References 33 publications

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“…Some models and algorithms are addressed to modelling fire propagation in order to minimise the forest fires damage. The systems presented in References [17][18][19] incorporate topography, vegetation and meteorological conditions to predict the forest fire spreading. The work presented in Reference [20] applies GA to define the optimum fireline and location of firefighters on the landscape which minimises the forest fire damage.…”

Section: Related Workmentioning

confidence: 99%

Optimum Sensors Allocation for a Forest Fires Monitoring System

Azevedo

Brito

Lima

et al. 2021

Forests

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Every year forest fires destroy millions of hectares of land worldwide. Detecting forest fire ignition in the early stages is fundamental to avoid forest fires catastrophes. In this approach, Wireless Sensor Network is explored to develop a monitoring system to send alert to authorities when a fire ignition is detected. The study of sensors allocation is essential in this type of monitoring system since its performance is directly related to the position of the sensors, which also defines the coverage region. In this paper, a mathematical model is proposed to solve the sensor allocation problem. This model considers the sensor coverage limitation, the distance, and the forest density interference in the sensor reach. A Genetic Algorithm is implemented to solve the optimisation model and minimise the forest fire hazard. The results obtained are promising since the algorithm could allocate the sensor avoiding overlaps and minimising the total fire hazard value for both regions considered.

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Section: Related Workmentioning

confidence: 99%

Optimum Sensors Allocation for a Forest Fires Monitoring System

Azevedo

Brito

Lima

et al. 2021

Forests

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“…For example, Dabrowski et al (2022a) used the Rothermel model with an unknown parameter to inform velocity in the level set method by using an ensemble Kalman filter (EnKF). Similarly, Mallet et al (2009) and Alessandri et al (2021) simplified the velocity through formulas proposed by Fendell and Wolff (2001) and Lo (2012), respectively. None of these approaches utilizes a datadriven approach to learn the spatially and temporally varying front velocity over the entire domain, which is required for realistic front propagation in complex environments, especially where large fires create their own "weather".…”

Section: Introductionmentioning

confidence: 99%

Using echo state networks to inform physical models for fire front propagation

Yoo

Wikle

2023

Spatial Statistics

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“…Kou et al introduced an estimation method based on deep learning and data mining to learn data and evaluate models [4]. Angelo A et al used level set method to construct fire propagation model for estimating the speed and direction of fire propagation, which can be used to instruct how to estimate and put on the fire [5]. Kim et al implement a rotational UV sensor to detect the position of the fire resource, and calculate the accuracy of fire location bases on Bayesian estimation [6].…”

Section: Introductionmentioning

confidence: 99%

Framework of Electrical Fire Probability Estimation Based on Bayesian Network Model Inference

Chen²,

Yin³

et al. 2021

Advances in Transdisciplinary Engineering

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Electrical fire had become one of the main parts in total fire accidents. Most of researches rely on the complex combustion models, which consume a huge number of computational resources. However, few studies focus on evaluating fire disaster by probability theory, and estimate the likelihood of fire occurring by the calculation result of probability based on the current data from the sensor. Bayesian Network is introduced due to the advantage of calculation complexity, ability of expressing uncertain factors and the accuracy of model with incomplete data. Some problems should be solved before using Bayesian Network to inference events based on given evidences. In this paper, the structure and the parameter of the Bayesian Network is created by the discussing result of the experts and scholars in electrical fire research field. A frequently-used fuzzy function called Sigmoid function to process data from raw data to the probability. Inference result by Bayesian Network is calculated by the Variable Elimination algorithm. A case study about the simulation of analyzing the probability of electrical fire happened when the load of circuit is under the high status. Research result shows that Bayesian Network model is suitable for estimating and analyzing in the scenario of electrical fire. Model has a good robust to express probability of electrical fire probability, which is of vital importance for estimating whether the fire occurs or not, thus providing significant information and instruction for preventing electrical fire and the sustainability of the environment. Based on the simulation result, it can conclude that the Bayesian network model inference is suitable for the electrical fire estimation scenario, and the introducing of this scheme is possible for predict electrical fire.

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Parameter estimation of fire propagation models using level set methods

Cited by 28 publications

References 33 publications

Optimum Sensors Allocation for a Forest Fires Monitoring System

Optimum Sensors Allocation for a Forest Fires Monitoring System

Using echo state networks to inform physical models for fire front propagation

Framework of Electrical Fire Probability Estimation Based on Bayesian Network Model Inference

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