A cellular automata model for forest fire spread prediction: The case of the wildfire that swept through Spetses Island in 1990

Alexandridis, Alex; Vakalis, D.; Siettos, Constantinos; Bafas, George

doi:10.1016/j.amc.2008.06.046

Cited by 154 publications

(139 citation statements)

References 10 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%

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%

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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“…산불확산을 예측하 고 그 동역학적 특성을 이해하기 위해서는 이러한 요 소들간의 상관관계를 규명해야 하는데 많은 비용부담 과 환경적 제약, 그리고, 요소들 간의 복잡한 상호 작 용 때문에 어려움이 있다 (Alexandridis et al, 2008).…”

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Effects of Geological Structure and Tree Density on the Forest Fire Patterns

Song¹,

Kwon²,

Lee³

2014

Korean Journal of Agricultural and Forest Meteorology

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Understanding the forest fire patterns is necessary to comprehend the stability of the forest ecosystems. Thus, researchers have suggested the simulation models to mimic the forest fire spread dynamics, which enables us to predict the forest damage in the scenarios that are difficult to be experimentally tested in laboratory scale. However, many of the models have the limitation that many of them did not consider the complicated environmental factors, such as fuel types, wind, and moisture. In this study, we suggested a simple model with the factors, especially, the geomorphological structure of the forest and two types of fuel. The two fuels correspond to susceptible tree and resistant tree with different probabilities of transferring fire. The trees were randomly distributed in simulation space at densities ranging from 0.5 (low) to 1.0 (high). The susceptible tree had higher value of the probability than the resistant tree. Based on the number of burnt trees, we then carried out the sensitivity analysis to quantify how the forest fire patterns are affected by the structure and tree density. We believe that our model can be a useful tool to explore forest fire spreading patterns.

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“…The model evolves in discrete time steps, with the cell states updated synchronously. The discrete and dynamic nature of CA models allows broad applications in a variety of natural phenomena and human activities, such as urban planning (Li et al, 2014), resource management (Bone and Dragićević, 2010), forest fire spread prediction (Alexandridis et al, 2008;Yassemi et al, 2008), plant invasion (Cannas et al, 2003), insect propagation (Bone et al, 2006;Pérez and Dragićević, 2011), animal migration patterns (Bennett and Tang, 2006), and landscape ecology (Parker et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance

Huang

et al. 2017

Ecological Modelling

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Huabing, "Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance" (2017 Cellular automata (CA) is a powerful tool for modeling the evolution of macroscopic scale phenomena as it couples time, space, and variables together while remaining in a simplified form. However, such application has remained challenging in forest insect epidemics due to the highly dynamic nature of insect behavior. Recent advances in temporal trajectory-based image analysis offer an alternative way to obtain high-frequency model calibration data. In this study, we propose an insect-CA modeling framework that integrates cellular automata, remote sensing, and Geographic Information System to understand the insect ecological processes, and tested it with measured data of mountain pine beetle (MPB) in the Rocky Mountains. The overall accuracy of the predicted MPB mortality pattern in the test years ranged from 88% to 94%, which illuminates its effectiveness in modeling forest insect dynamics. We further conducted sensitivity analysis to examine responses of model performance to various parameter settings. In our case, the ensemble random forest algorithm outperforms the traditional linear regression in constructing the suitability surface. Small neighborhood size is more effective in simulating the MPB movement behavior, indicating that short-distance is the dominating dispersal mode of MPB. The introduction of a stochastic perturbation component did not improve the model performance after testing a broad range of randomness degree, reflecting a relative compact dispersal pattern rather than isolated outbreaks. We conclude that CA with remote sensing observation is useful for landscape insect movement analyses; however, consideration of several key parameters is critical in the modeling process and should be more thoroughly investigated in future work.

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A cellular automata model for forest fire spread prediction: The case of the wildfire that swept through Spetses Island in 1990

Cited by 154 publications

References 10 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

Effects of Geological Structure and Tree Density on the Forest Fire Patterns

Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance

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