基于贝叶斯网络的城市地震次生灾害演化机理分析
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摘要
为了解城市地震次生灾害的演化路径及扩散机理,进而为城市防震救灾工作提供科学的参考依据,通过对城市地震有关资料和文献的分析,确定出城市地震次生灾害演化系统贝叶斯网络的节点变量及取值范围,并根据节点变量之间的因果关系构建出贝叶斯网络结构图。通过对国内外典型震例中的16个受灾城市相关数据进行统计,分析获得网络结构各个变量的条件概率。结合贝叶斯网络推理知识,利用贝叶斯网络工具箱分析了在相同的地震输入变量、不同的控制输入变量取值条件下,各类城市地震次生灾害的发生概率。算例分析结果表明不同的应急管理水平对城市地震各类次生灾害的发生概率有显著影响。
In order to understand the evolution routes and diffusion mechanism of earthquake-induced urban disasters and provide reference for seismic surveillance and prediction,node variables and their value ranges of the Bayesian network for evolution system of earthquake-induced urban disasters are determined based on analysis on relative data and literatures,and a Bayesian network structure diagram is established according to the causality of node variables.Conditional probability of each variable in the network is acquired by data statistics of 16 cities affected by representative earthquakes at home and abroad.Finally,posterity probabilities of secondary disasters under the condition of the same input variables and different environment variables are obtained by using Bayesian network toolbox based on Bayesian network reasoning.The results of an illustrated example show that the level of emergency management has an important impact on the occurrence probability of different earthquake-induced urban disasters.
In order to understand the evolution routes and diffusion mechanism of earthquake-induced urban disasters and provide reference for seismic surveillance and prediction,node variables and their value ranges of the Bayesian network for evolution system of earthquake-induced urban disasters are determined based on analysis on relative data and literatures,and a Bayesian network structure diagram is established according to the causality of node variables.Conditional probability of each variable in the network is acquired by data statistics of 16 cities affected by representative earthquakes at home and abroad.Finally,posterity probabilities of secondary disasters under the condition of the same input variables and different environment variables are obtained by using Bayesian network toolbox based on Bayesian network reasoning.The results of an illustrated example show that the level of emergency management has an important impact on the occurrence probability of different earthquake-induced urban disasters.
引文
[1]余世舟,赵振东,钟江荣.基于GIS的地震次生灾害数值模拟[J].自然灾害学报,2003,12(4):100-105.
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[11]宋利萍,张俊玲,柏琳,等.城市地震成灾机制分析[J].高原地震,2005,17(2):38-43.
[12]董磊磊.基于贝叶斯网络的突发事件链建模研究[D].大连:大连理工大学,2009.
[13]马玉宏,谢礼立.我国社会可接受地震人员死亡率的研究[J].自然灾害学报,2001,10(3):56-73.
[14]李杰,江建华.城市地震次生火灾危险性分析[J].自然灾害学报,2000,5(9):87-92.
[15]蒋望东,林士敏.基于贝叶斯网络工具箱的贝叶斯学习和推理[J].信息技术,2007(2):5-8.
[16]程小平,邱玉辉.贝叶斯网学习算法模型及参数学习算法[J].计算机科学,1999,26(2):52-54.
[17]Cooper G F.The computational complexity of probabilistic infer-ence using Bayesian belief networks[J].Artificial Intelligence,1990,42:393-405.
[2]Scawthorn C,Yamada Y,Iemura H.A model for urban post-earth-quake fire hazard[J].Disasters,1981,5(2):125-132.
[3]Li J,Jiang J H,Li M H.Hazard analysis system of urban post-earthquake fire based on GIS[J].ACTA Seismologica Sinica,2001,14(4):448-455.
[4]赵思健,任爱珠,熊利亚.城市地震次生火灾研究综述[J].自然灾害学报,2006,15(2):57-67.
[5]陈素文,李国强.地震次生火灾的研究进展[J].自然灾害学报,2008,17(5):120-126.
[6]赵思健,熊利亚,任爱珠.基于GIS的城市地震次生火灾仿真系统[J].防灾减灾工程学报,2007,27(1):43-51.
[7]赵振东,余世舟,钟江荣.地震次生毒气泄漏与扩散的数值模拟与动态仿真[J].地震工程与工程振动,2002,22(5):137-142.
[8]余世舟,赵振东,钟江荣.地震次生毒气泄漏与扩散数值模拟的参数分析[J].地震工程与工程振动,2002,22(6):150-155.
[9]高乃辉,赵鸣,李素贞,等.汶川8.0级地震都江堰市天然气系统震害分析及救灾过程[J].灾害学,2010,25(3):71-76.
[10]孟于飞,郭恩栋,刘红丽.城市燃气管网的地震紧急处置系统研究[J].灾害学,2010,25(S1):223-226.
[11]宋利萍,张俊玲,柏琳,等.城市地震成灾机制分析[J].高原地震,2005,17(2):38-43.
[12]董磊磊.基于贝叶斯网络的突发事件链建模研究[D].大连:大连理工大学,2009.
[13]马玉宏,谢礼立.我国社会可接受地震人员死亡率的研究[J].自然灾害学报,2001,10(3):56-73.
[14]李杰,江建华.城市地震次生火灾危险性分析[J].自然灾害学报,2000,5(9):87-92.
[15]蒋望东,林士敏.基于贝叶斯网络工具箱的贝叶斯学习和推理[J].信息技术,2007(2):5-8.
[16]程小平,邱玉辉.贝叶斯网学习算法模型及参数学习算法[J].计算机科学,1999,26(2):52-54.
[17]Cooper G F.The computational complexity of probabilistic infer-ence using Bayesian belief networks[J].Artificial Intelligence,1990,42:393-405.
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