Simulation of forest fire fronts using cellular automata

Encinas, Ascensión Hernández; Encinas, Luis Hernández; White, Sara Hoya; Rey, Ángel Martín del; Sánchez, Gerardo Rodríguez

doi:10.1016/j.advengsoft.2006.09.002

Cited by 88 publications

(16 citation statements)

References 11 publications

(12 reference statements)

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“…CA, originally developed by von Neumann [5] as models of selfreproducing systems, have been extensively used to model and simulate environmental and geophysical systems [6][7][8][9][10][11][12][13]. CA proved to be a promising model for the modelling of earthquakes [14], since certain aspects of the earthquake dynamics, function and evolution can be simulated using several mathematical tools introduced through the use of CA.…”

Section: Introductionmentioning

confidence: 99%

Parametric optimisation in a 2-D cellular automata model of fundamental seismic attributes with the use of genetic algorithms

Georgoudas

Scordilis

Andreadis

2011

Advances in Engineering Software

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Section: Introductionmentioning

confidence: 99%

Parametric optimisation in a 2-D cellular automata model of fundamental seismic attributes with the use of genetic algorithms

Georgoudas

Scordilis

Andreadis

2011

Advances in Engineering Software

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“…The results highlight the effectiveness of ABMS applied in open environments. Other examples of the advantages of using ABSM in rescue operations in open and dynamical environments were provided by Hernandez Encinas et al (2007) and Abramson et al (2008). The decision-making systems have generated high expectation during the last decade, becoming more complex and functional due to their great ability to hand large amounts of information for their specific analyses.…”

Section: Agent-based Systemsmentioning

confidence: 99%

Optimizing urban traffic control using a rational agent

Martínez

Rocha

Laria-Menchaca

2014

J. Zhejiang Univ. - Sci. C

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This paper is devoted to developing and evaluating a set of technologies with the objective of designing a methodology for the implementation of sophisticated traffic lights by means of rational agents. These devices would be capable of optimizing the behavior of a junction with multiple traffic signals, reaching a higher level of autonomy without losing reliability, accuracy, or efficiency in the offered services. In particular, each rational agent in a traffic signal will be able to analyze the requirements and constraints of the road, in order to know its level of demand. With such information, the rational agent will adapt its light cycles with the view of accomplishing more fluid traffic patterns and minimizing the pollutant environmental emissions produced by vehicles while they are stopped at a red light, through using a case-based reasoning (CBR) adaptation. This paper also integrates a microscopic simulator developed to run a set of tests in order to compare the presented methodology with traditional traffic control methods. Two study cases are shown to demonstrate the efficiency of the introduced approach, increasing vehicular mobility and reducing harmful activity for the environment. For instance, in the first scenario, taking into account the studied traffic volumes, our approach increases mobility by 23% and reduces emissions by 35%. When the roads are managed by sophisticated traffic lights, a better level of service and considerable environmental benefits are achieved, demonstrating the utility of the presented approach.

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“…Despite producing good simulation results, models that are based on discrete values can most likely have discrepancies (Hernández Encinas et al, 2007). One of the main issues of the presented models is that the weather variable incorporated only refers to the velocity and direction of the wind.…”

Section: Forest Fire Spread Modelsmentioning

confidence: 99%

Urban Fire Spread Modelling and Simulation Using Cellular Automaton With Extreme Learning Machine

Patac

Vicente

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Urban fire continues to be a persistent disaster, especially with the proliferation of highly dense urban settlements. As a response, several measures were established to help mitigate the losses caused by fire including simulating the fire spread. The cellular automaton system has been widely used to simulate the complex process of fire development along with Physics-based models. A data-driven approach has been rarely employed. This paper presents the result of incorporating machine learning techniques to the existing cellular automaton based urban fire spread models. Specifically, instead of manually calculating the ignition probability of each cell in the automaton, the Extreme Learning Machine (ELM) was used to learn the ignition probability from the historical data. After building the model, its performance was evaluated using the data collected from the four fires in Basak, Lapu-Lapu City. By using a confusion matrix to compare the actual and the predicted values, the Burned Actual – Burned Predicted relationship was derived. Results suggest that the proposed method can effectively describe the development of fire, and the model accuracy is quite good (i.e., the Burned Actual - Burned Predicted relationship ranges from 78% to 83%). Lastly, the study was able to demonstrate the possibility of using a data-driven approach in creating a simple cellular automaton fire spread simulation model for urban areas. Further studies utilizing more fire incident data on with varying properties is recommended.

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Simulation of forest fire fronts using cellular automata

Cited by 88 publications

References 11 publications

Parametric optimisation in a 2-D cellular automata model of fundamental seismic attributes with the use of genetic algorithms

Parametric optimisation in a 2-D cellular automata model of fundamental seismic attributes with the use of genetic algorithms

Optimizing urban traffic control using a rational agent

Urban Fire Spread Modelling and Simulation Using Cellular Automaton With Extreme Learning Machine

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