为量化森林草原火灾蔓延过程中,周边重要保护目标(如输电线路、危化品储罐、核设施等)所受到的动态威胁,该文提出一种融合数据驱动火蔓延预测与动态Bayes网络(dynamic Bayesian network,DBN)的火场风险评估方法,重点解决火灾蔓延的各类影响因素随时空环境持续变化带来的风险预测误差累积问题。首先将火场及其周边重要保护目标离散为节点,构建包含时序自相关与空间蔓延依赖的DBN结构,并将火蔓延速率和火强度预测结果输出映射为节点间条件概率与载体失效概率模型参数,随后动态修正网络中的条件概率,实现受灾失效概率的时变推演。针对仅存在单一承灾载体的风险场景,使用所提方法预测出：在多种燃料可燃性和风速的组合工况下,输电线路、危化品储罐和核设施的24 h失效概率的最大增幅分别为22%、68%和20%;针对存在多个保护目标的风险场景,使用所提方法预测出：在野火蔓延条件下,不同重要保护目标结构差异性和与火线距离远近导致输电线路、危化品储罐和核设施的24 h失效概率分别达到0.24、0.14和0.53。在此基础上,引入隔离带开设策略,随着对蔓延速率的抑制效能增大,输电线路节点受灾失效概率可降至0,核设施可降至0.12,而危化品储罐由于分布情况和自身结构存在差异,受灾失效概率并未显著下降。结果表明,所提方法能够在火情与环境持续变化条件下动态更新不确定性传播过程,为干预策略评估提供定量依据。
Abstract
[Objective] Rapid and accurate assessment of dynamic threats to surrounding assets, such as transmission lines, chemical storage tanks and nuclear facilities, during the propagation of forest-grassland wildfires is critical for emergency response and disaster mitigation. Traditional fire risk assessment methods typically rely on static hazard maps or single-time fire-front predictions and therefore fail to adequately account for the continuous spatiotemporal evolution of fire behavior influenced by changing environmental factors (e.g., wind and fuel), leading to cumulative error propagation in dynamic risk prediction. Furthermore, the complex and heterogeneous failure mechanisms of different infrastructure types are seldom incorporated into a unified, dynamically updating risk framework. Therefore, this study aims to develop a novel, integrated risk assessment method capable of continuously updating infrastructure failure probabilities as wildfire intensity and extent evolve. [Methods] The proposed framework synergistically integrates a data-driven wildfire spread prediction model with a dynamic Bayesian network (DBN). The fire-affected landscape and key protected assets were first discretized into interconnected spatial nodes. A DBN structure was then constructed to capture two fundamental dependencies: the temporal autocorrelation of fire conditions at each node (how the state evolves) and the spatial dependencies between adjacent nodes (how fire spreads from one location to another). The core parameters of this DBN were informed by a data-driven fire spread model. Specifically, predictions of key fire behavior metrics, namely, spread rate and fireline intensity, were generated for each node. Then, DBN conditional probabilities were updated over time using the predicted spread rate and the fireline intensity, which were further mapped to asset-specific fragility models to infer evolving failure probabilities. [Results] Using the proposed framework, we built a kilometer-scale synthetic fire scenario using every single protected asset and showed that the 24-h failure probabilities of transmission lines, chemical tanks, and nuclear facilities increased by up to 22%, 68% and 20% across wind-fuel scenarios. In addition, a case study based on observations from the 2022 Oak Fire (California, USA) was conducted using multiple protected assets. The 24-h failure probabilities reached 0.24 for transmission lines, 0.14 for hazardous-chemical tanks, and 0.53 for the nuclear facility. Furthermore, we evaluated a firebreak construction strategy: as the suppression effect on the rate of spread increased, the failure probability could be reduced to as low as 0 for transmission-line nodes and to 0.12 for the nuclear facility, whereas the probability reduction for hazardous-chemical tanks was not significant due to their spatial distribution and structural differences. [Conclusions] This study proposes a novel dynamic wildfire risk assessment framework that integrates data-driven spread prediction with probabilistic graphical modeling. By continuously updating a DBN using fire behavior model outputs, the proposed approach effectively mitigates cumulative prediction errors in a spatiotemporally evolving environment and provides quantitative support for rapid response, emergency resource allocation, and intervention strategy assessment.
关键词
森林草原火灾 /
重要保护目标 /
动态Bayes网络 /
风险评估 /
消防干预
Key words
wildfire /
key protected assets /
dynamic Bayesian network /
failure probability /
fire intervention
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基金
“十四五”国家重点研发计划项目(2023YFC3006900)
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