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清华大学学报(自然科学版)  2023, Vol. 63 Issue (6): 926-933    DOI: 10.16511/j.cnki.qhdxxb.2023.22.016
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姜文宇1,2, 王飞1,2, 苏国锋1, 乔禹铭1,2, 李鑫3, 权威4
1. 清华大学 工程物理系, 北京 100084;
2. 清华大学 深圳国际研究生院, 安全科学与技术研究所, 深圳 518000;
3. 佛山市城市安全研究中心, 佛山 528000;
4. 应急管理部 森林消防局, 北京 100081
Dynamic modeling approach for suppression firing based on cellular automata
JIANG Wenyu1,2, WANG Fei1,2, SU Guofeng1, QIAO Yuming1,2, LI Xin3, QUAN Wei4
1. Department of Engineering Physics, Tsinghua University, Beijing 100084, China;
2. Institute of Safety Science and Technology, Tsinghua Shenzhen International Graduate School, Shenzhen 518000, China;
3. Foshan Urban Safety Research Center, Foshan 528000, China;
4. Forest Fire Administration, Ministry of Emergency Management, Beijing 100081, China
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摘要 以火灭火是控制森林火灾蔓延的重要手段之一。然而,现有以火灭火大多依赖专家经验,鲜有定量化分析的相关研究。该文构建了一种用于以火灭火建模分析的定量化火灾蔓延预测模型。该模型采取元胞自动机算法将林火动态蔓延过程定义为网格动力学问题,实现对复杂环境中森林火灾时空动态演化的定量化建模。该模型提出了一种用于以火灭火的网格状态触发机制,将反向点火行为定义为含特定时间触发约束的因素,进而实现对不同时空条件下人为点火因素的定量化模拟分析。为了验证模型的有效性,以2022年8月重庆市北碚区森林火灾为研究案例,分析了不同条件下实施以火灭火策略的效果。实验结果表明:所提出的定量化分析模型能够为森林灭火队伍制订以火灭火策略提供科学辅助,有助于提升森林火灾应急管理的现代化水平。
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关键词 森林火灾以火灭火元胞自动机火灾建模应急管理    
Abstract:[Objective] Suppression firing is a crucial approach to control the spread of forest fires. However, existing suppression firing mainly relies on rare quantitative analysis by experts, making efficient forest fire control efforts difficult to perform.[Methods] In this paper, a fire spread prediction model was implemented to quantitatively simulate and analyze suppression firing. This model adopted the cellular automata algorithm to define the fire spread as a grid dynamics problem. The forest landscape was divided into contiguous regular cells with different cell burning states (S0: unburned; S1: ignited; S2: flashover; S3: extinguishing; S4: extinguished). Then, multimodal environmental factors such as fuel type, slope, wind, and temperature were considered to construct the rate of the spread function and predict the fire spread speed in various complex scenarios. Next, state update rules were proposed to define how the burning state of forest cells was transformed for different fire conditions. The minimum travel time method was then adopted to iteratively calculate the ignition time of each cell in the forest landscape. Therefore, the spatiotemporal evolution of forest fires in complex environmental scenarios was quantitatively modeled. Additionally, a trigger mechanism was proposed to define reverse ignition behavior as a grid cell with specific time-trigger constraints. This mechanism realized a quantitative simulation analysis of human ignition factors with different spatiotemporal conditions.[Results] To verify the reliability and feasibility of our model, a real forest fire that occurred in the Beibei District of Chongqing in August, 2022 was chosen as the study case. Fire data (fuel type, slope, historical weather, fire perimeter, etc.) and firefighting records (the location and time of fire ignition, suppression firing description, etc.) were collected to reconstruct the firing process. Our model was applied to the suppression firing in this forest fire to analyze the fire control effect for different environmental conditions. The experimental results showed that our model was superior in predicting the spatiotemporal spread of forest fire with competitive model performance (Jaccard: 0.732; Sorensen: 0.845). The spatial location and ignition time of the reverse ignition in suppression firing were quantitatively analyzed and visualized, demonstrating how the reverse fire burned the fuel in advance and impeded the spread of free fires.[Conclusions] Quantitatively modeling the suppression firing can provide effective decision-making for wildfire firefighters to formulate accurate fire control strategies and improve the modernization capability of forest fire management. As a highly complex, dangerous firefighting strategy, more research on the combustion mechanism and simulation method of suppression firing is needed, such as the formation mechanism and modeling method of local microclimate in a forest fire landscape, the barrier effect of the isolation zone, and spatial optimization.
Key wordsforest fire    suppression firing    cellular automata    fire modeling    emergency management
收稿日期: 2022-12-12      出版日期: 2023-05-12
通讯作者: 王飞,副教授,     E-mail:
作者简介: 姜文宇(1995—),男,博士研究生。
姜文宇, 王飞, 苏国锋, 乔禹铭, 李鑫, 权威. 基于元胞自动机的以火灭火动态建模方法[J]. 清华大学学报(自然科学版), 2023, 63(6): 926-933.
JIANG Wenyu, WANG Fei, SU Guofeng, QIAO Yuming, LI Xin, QUAN Wei. Dynamic modeling approach for suppression firing based on cellular automata. Journal of Tsinghua University(Science and Technology), 2023, 63(6): 926-933.
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