Journal of System Simulation
Abstract
Abstract: According to the characteristics of fire behavior with strong sudden, disposal of difficult rescue, and heavy destructiveness, combining dynamic data system and discrete event system specification model, a dynamic data driven forest fire spread model is put forward based on devs modeling. The dynamic data system needs to be realized through the interaction between the computer software and the data characteristics of each environment, which is characterized by flexible data and real data. DEVS supports object-oriented modeling, and can make the modeling and simulation of the system modular, hierarchical and formal. The model is set up according to the dynamic data system and discrete event system specification model in forest fire spread simulation application in their structural characteristics, and combined with the specific functional requirements of users of the test region system construction. The model is applied to a mountain field with forest fire simulation process, the model to simulate is close to actual situation, with the height of the true similarity.
Recommended Citation
Zhou, Guoxiong; Yin, Kejia; and Chen, Aibin
(2019)
"Dynamic Data Modeling Driven Model for Forest Fire Spread Based on DEVS,"
Journal of System Simulation: Vol. 30:
Iss.
10, Article 6.
DOI: 10.16182/j.issn1004731x.joss.201810006
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol30/iss10/6
First Page
3642
Revised Date
2016-08-29
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.201810006
Last Page
3647
CLC
TP273
Recommended Citation
Zhou Guoxiong, Yin Kejia, Chen Aibin. Dynamic Data Modeling Driven Model for Forest Fire Spread Based on DEVS[J]. Journal of System Simulation, 2018, 30(10): 3642-3647.
DOI
10.16182/j.issn1004731x.joss.201810006
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Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons