A model for predicting forest fire spreading using cellular automata

Karafyllidis, Ioannis; Thanailakis, A.

doi:10.1016/s0304-3800(96)01942-4

Cited by 314 publications

(141 citation statements)

References 15 publications

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“…CA-based model has been a mature approach to model the spread of forest wildfires [22][23][24]49]. In the CA-based models, the geographic landscape is firstly divided into a 2D array of identical square units and each unit is represented by a cell agent in the model.…”

Section: "Domain Parallel" Model Simulation Case Studymentioning

confidence: 99%

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4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Guan

et al. 2016

IJGI

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Significant computation challenges are emerging as agent-based modeling becomes more complicated and dynamically data-driven. In this context, parallel simulation is an attractive solution when dealing with massive data and computation requirements. Nearly all the available distributed simulation systems, however, do not support geospatial phenomena modeling, dynamic data injection, and real-time visualization. To tackle these problems, we propose a distributed dynamic-data driven simulation and analysis system (4D-SAS) specifically for massive spatial agent-based modeling to support real-time representation and analysis of geospatial phenomena. To accomplish large-scale geospatial problem-solving, the 4D-SAS system was spatially enabled to support geospatial model development and employs high-performance computing to improve simulation performance. It can automatically decompose simulation tasks and distribute them among computing nodes following two common schemes: order division or spatial decomposition. Moreover, it provides streaming channels and a storage database to incorporate dynamic data into simulation models; updating agent context in real-time. A new online visualization module was developed based on a GIS mapping library, SharpMap, for an animated display of model execution to help clients understand the model outputs efficiently. To evaluate the system's efficiency and scalability, two different spatially explicitly agent-based models, an en-route choice model, and a forest fire propagation model, were created on 4D-SAS. Simulation results illustrate that 4D-SAS provides an efficient platform for dynamic data-driven geospatial modeling, e.g., both discrete multi-agent simulation and grid-based cellular automata, demonstrating efficient support for massive parallel simulation. The parallel efficiency of the two models is above 0.7 and remains nearly stable in our experiments.

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Section: "Domain Parallel" Model Simulation Case Studymentioning

confidence: 99%

“…The other was a forest fire propagation model. It is a cellular automata-based model that expresses the dynamics of forest fires spreading on a mountainous landscape [22][23][24]. This model was used to validate the "domain parallel" method.…”

Section: Introductionmentioning

confidence: 99%

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Guan

et al. 2016

IJGI

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“…In a (CA) model, the activity is measured at global level, from a set of active cells and a common optimization consists in focusing the state transitions on it. The active cells are then defined as the components of the (CA) model that at time t will possibly change state, or will definitely be left unchanged between two consecutive transitions [17,29]. The local level informs the global level on the model activity.…”

Section: Activity Preceptsmentioning

confidence: 99%

Randomizing activity in fire spreading simulations

Innocenti

Cancellieri²

2013

ITM Web of Conferences

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Abstract. In this paper, we suggest the use of activity paradigm to model the uncertain and imprecise nature of the local dynamics in fire spreading simulations. Formerly based on a very simple set of rules, Cellular Automata (CA) models are remarkable in their ability to easily simulate fire spreading behaviour. A new algorithm to track activity concept in a (CA) model is presented. It is called random-activity and it combines activity and stochastic precepts. This algorithm enhances the realism of the spatial simulations. It resolves the problem of the spurious symmetries of the square lattice usually encountered which affect negatively outputs. Experiment shows that the approach enhances fire spreading simulations which are based on local description of the fire dynamics with general descriptive laws. A comparison between classical and random-activity algorithms is performed. The random-activity algorithm proposed outperforms the classical algorithm in terms of graphical restitutions' realism. It might be used to develop fire riskmanagement tools. A new way is presented to combine activity concept and stochastic modelling in a (CA) model.

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“…Cellular automata [36] have been widely used in the last decades for environmental simulations such as modeling snow-cover dynamics [26] or snow avalanches [2], simulating land features dynamics [25] or multiple land use changes using GIS [27], modeling dynamic spatial in GIS [29], characterization of natural textures [24], modeling vegetation systems dynamics [3], detecting Vibrio cholerae by indirect measurement [28], modeling 3D clouds [23], simulating forest fire spread [21] [30], understanding modular illumination systems [4], modeling lava flows [34], understanding of urban growth [9] and architectural design [8], modelling of vehicular traffic [1], classifying of satellite images [7] [19], projecting population percentages infected by periodic plague [18], and simulating species competition and evolution [10]. This paper focuses on RACA (RAinfall with Cellular Automata), a new algorithm, developed by the authors, that simulates the complete rainfall process in the DEM satellite images.…”

Section: Introductionmentioning

confidence: 99%

Simulating rainfall, water evaporation and groundwater flow in three-dimensional satellite images with cellular automata

Espínola

Piedra-Fernández

Ayala

et al. 2016

Simulation Modelling Practice and Theory

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Remote sensing has been used in numerous environmental simulations with the aim of solving and improving many different kinds of problems, e.g., meteorology applications, soil quality studies, water resource exploration, and environmental protection. Besides, cellular automata have been widely used in the field of remote sensing for simulating natural phenomena over two-dimensional satellite images. However, simulations on Digital Elevation Models (DEM), or three-dimensional (3D) satellite images, are scarce. This paper presents a study of modeling and simulation of the weather phenomena of rainfall, water evaporation and groundwater flow in 3D satellite images through a new algorithm, developed by the authors, named RACA (RAinfall with Cellular Automata). The purpose of RACA is to obtain, from the simulation, numerical and 3D results related to the total cumulative flow and maximum level of water that allow us to make decisions on important issues such as analyzing how climate change will affect the water level in a particular area, estimating the future water supply of a population, establishing future construction projects and urban planning away from locations with high probability of flooding, or preventing the destruction of property and human life from future natural disasters in urban areas with probability of flooding.

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A model for predicting forest fire spreading using cellular automata

Cited by 314 publications

References 15 publications

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Randomizing activity in fire spreading simulations

Simulating rainfall, water evaporation and groundwater flow in three-dimensional satellite images with cellular automata

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