台风风暴潮情景构建与时空推演

doi:10.12082/dqxxkx.2020.190604

摘要/Abstract

摘要：

因台风风暴潮的突发性、情景演变时间的连续性和路径的不确定性,导致应急决策者在应急救援中难以做出正确决策,针对这一现状,将“情景—应对”应用在台风风暴潮应急决策中。本文在分析台风风暴潮情景、情景要素的概念模型基础上,首先通过资料搜集、属性识别等方法提取关键情景要素,采用框架表示法构建情景;然后分析台风风暴潮情景演变规律及演变路径;其次通过动态贝叶斯网络法构建台风风暴潮动态情景网络;最后利用先验概率与条件概率计算情景状态概率,实现了台风风暴潮的关键情景推演。本文以2018年9月16日11时至17时山竹台风对广东省沿海城市影响为例,演示了台风风暴潮的情景推演流程及关键技术。实证分析结果表明,溃堤、海水倒灌、洪水、滑坡发生的概率分别为85%、81%、74%、54%,验证了情景推演在风暴潮中应用的合理性。

关键词: 台风风暴潮, 情景要素, 框架表示法, 情景演变, 演变路径, 动态贝叶斯网络, 情景推演, 应急决策

Abstract:

Due to abruptness of typhoon storm surge, continuity of the evolution time and uncertainty of the path, it is hard for emergency decision-makers to make correct decisions in emergency rescue. To solve this problem, this article applies "scenario-response" to the typhoon storm surge. Firstly, based on the analysis of the typhoon storm surge scenarios and the conceptual model of the scenario elements, we extract the key scenario elements by means of data collection and attribute recognition. Then, we construct the dynamic scenario network of the typhoon storm surge by the method of frame representation. Secondly, we analysis the evolution and path of typhoon storm surge. Thirdly, we construct dynamic scenario network of typhoon storm surge with the dynamic Bayesian network method. Finally, we calculate the state probability of scenarios with the prior state probability and conditional probability and realize the key scenario deduction of the typhoon storm surge. In the end of the essay, we simulated an experiment for the influence of typhoon on the coastal cities of Guangdong Province from 11 to 17 on September 16 in 2018. The experiment results show that the probability of dykes, seawater inversion, floods and landslides respectively are 85%, 81%, 74%, 54%. The conclusion is drawn as follows: (1) The structure and content of each scenario element in the scenario construction process are different and interactional. Frame representation can reasonably characterize complex heterogeneous scenario elements data. (2) The evolution path of the situation is determined by many factors such as the situation itself, the disaster-bearing body, and emergency management. Decision makers need to comprehensively consider the emergency team and the rational use of resources when making decisions. (3) From the construction of the storm surge scenario to the deduction, the whole process has clear ideas and intuitive results, which is conducive to the promotion and application in marine disasters. The tentative application of "scenario-response" in storm surge events provides new emergency ideas and solutions for storm surge control.

Key words: typhoon storm surge, scenario elements, frame representation, scenario evolution, evolution path, dynamic Bayesian network, scenario deduction, emergency decision

饶文利, 罗年学. 台风风暴潮情景构建与时空推演[J]. 地球信息科学学报, 2020, 22(2): 187-197.DOI:10.12082/dqxxkx.2020.190604

RAO Wenli, LUO Nianxue. Scenarios Construction and Spatial-temporal Deduction of Typhoon Storm Surge[J]. Journal of Geo-information Science, 2020, 22(2): 187-197.DOI:10.12082/dqxxkx.2020.190604

图/表 12

图1

图2

图3

表1

图4

表2

山竹事件情景链（部分推演）"

情景名称	承灾体	应急管理
山竹台风 $(S 0)$	广东沿海城市 $(A 0)$	建立应急指挥救援小组,启动预案 $(D 0)$
溃堤 $(S 1)$	广东沿海城市 $(A 1)$	加紧抢修溃堤口,加固堤坝 $(D 1)$
事件消失 $(S 2)$	广东沿海城市
海水倒灌 $(S 3)$	广东沿海城市 $(A 3)$	紧急疏散、转移可能倒灌区域人员 $(D 3)$
洪水 $(S 4)$	广东沿海城市 $(A 4)$	蓄洪滞洪分洪,人员转移安置 $(D 4)$
事件消失 $(S 5)$	广东沿海城市
滑坡 $(S 6)$	广东沿海城市 $(A 6)$	撤离危险区人员,疏通排水渠道,引导泥石流顺畅流动 $(D 6)$
事件消失 $(S 7)$	广东沿海城市

表2

图5

图6

图7

表3

网络节点变量类型与取值集合"

节点名称	类型	取值集合
情景状态 $(S)$	布尔变量	{真 $(T)$ ,假 $(F)$ }
承灾体 $(A)$	二值顺序变量	{积极 $(P)$ ,消极 $(N)$ }
应对措施 $(D)$	布尔变量	{真 $(T)$ ,假 $(F)$ }

表3

表4

网络节点的先验概率与条件概率（部分数据）"

情景	节点变量	先验概率		条件概率
山竹台风	$S 0$	$P (D 0 = T)$	0.95	$P (S 0 = T \| D 0 = T, A 0 = P)$	0.95
		$P (D 0 = F)$	0.05	$P (S 0 = T \| D 0 = T, A 0 = N)$	0.80
		$P (A 0 = P)$	0.70	$P (S 0 = T \| D 0 = F, A 0 = P)$	0.70
		$P (A 0 = N)$	0.30	$P (S 0 = T \| D 0 = F, A 0 = N)$	0.40
溃堤	$S 1$	$P (D 1 = T)$	0.96	$P (S 1 = T \| D 1 = T, A 1 = P, S 0 = T)$	0.90
		$P (D 1 = F)$	0.04	$P (S 1 = T \| D 1 = T, A 1 = P, S 0 = F)$	0.70
		$P (A 1 = P)$	0.75	$P (S 1 = T \| D 1 = T, A 1 = N, S 0 = T)$	0.85
		$P (A 1 = N)$	0.25	$P (S 1 = T \| D 1 = T, A 1 = N, S 0 = F)$	0.60
		—	—	$P (S 1 = T \| D 1 = F, A 1 = P, S 0 = T)$	0.55
		—	—	$P (S 1 = T \| D 1 = F, A 1 = P, S 0 = F)$	0.40
		—	—	$P (S 1 = T \| D 1 = F, A 1 = N, S 0 = T)$	0.30
		—	—	$P (S 1 = T \| D 1 = F, A 1 = N, S 0 = F)$	0.25

表4

图8

参考文献 28

[1]	孙湘平 . 中国近海区域海洋[M]. 北京: 海洋出版社, 2006.
	[ Sun X P. Offshore seas of China[M]. Beijing: Ocean Press, 2006. ]
[2]	舒其林 . 非常规突发事件的情景演变及"情景–应对"决策方案生成[J]. 中国科学技术大学学报, 2012,42(11):936-941.
	[ Shu Q L . Study on scenario evolvement and alternative generation of "scenario-response" decision-making in unconventional emergencies[J]. Journal of University of Science and Technology of China, 2012,42(11):936-941. ]
[3]	Chang M S, Tseng Y L, Chen J W . A scenario planning approach for the flood emergency logistics preparation problem under uncertainty[J]. Transportion Research Part E: Logistics and Transportion Review, 2007,43(6):737-754.
[4]	Mahmoud M, Liu Y Q, Hartmann H . A formal framework for scenario development in support of environmental decision-making[J]. Environmental Modelling & Software, 2009,24(7):798-808.
[5]	吴倩, 谈伟, 盖文妹 . 基于动态贝叶斯网络的民航突发事件情景分析研究[J]. 中国安全生产科学技术, 2016,12(3):169-174.
	[ Wu Q, Tan W, Gai W M . Study on scenario analysis of civil aviation emergency based on dynamic Bayesian network[J]. Journal of Safety Science and Technology, 2016,12(3):169-174. ]
[6]	孙超, 钟少波, 邓羽 . 基于暴雨内涝灾害情景推演的北京市应急救援方案评估与决策优化[J]. 地理学报, 2017,72(5):804-816.
	[ Sun C, Zhong S B, Deng Y . Scenario deduction based emergency rescue plan assessment and decision optimization of urban rainstorm water-logging: A case study of Beijing[J]. Acta Geographica Sinica, 2017,72(5):804-816. ]
[7]	张恭孝, 杨荣华, 崔萌 , 等. 基于"情景–应对"模式的危险化学品泄漏应急决策系统的构建[J]. 化工环保, 2018,38(5):609-614.
	[ Zhang G X, Yang R H, Cui M , et al. Construction of the emergency decision system for hazardous chemicals leakage based on "scenario-response" model[J]. Environmental Protection of Chemical Industry, 2018,38(5):609-614. ]
[8]	徐元元 . 基于动态贝叶斯网络的高层建筑火灾应急决策研究[D]. 郑州:郑州大学, 2019.
	[ Xu Y Y . Study on the decision making of high-rise buildings fire emergency based on for on dynamic Bayesian network[D]. Zhengzhou: Zhengzhou University, 2019. ]
[9]	仲秋雁, 路光, 王宁 . 基于知识元模型和系统动力学模型的突发事件仿真方法[J]. 情报科学, 2014,32(10):15-19.
	[ Zhong Q Y, Lu G, Wang N . Simulation method research on emergencies based on system dynamics model and knowledge element model[J]. Information Science, 2014,32(10):15-19. ]
[10]	王循庆, 李勇建, 孙晓羽 . 基于演化博弈的危化品安全监管情景推演研究[J]. 中国安全生产科学技术, 2017,13(1):115-121.
	[ Wang X Q, Li Y J, Sun X Y . Study on scenario inference of hazardous chemicals safety supervision based on evolutionary game[J]. Journal of Safety Science and Technology, 2017,13(1):115-121. ]
[11]	宋英华, 刘含笑, 蒋新宇 , 等. 基于知识元与贝叶斯网络的食品安全事故情景推演研究[J]. 情报学报, 2018,37(7):712-720.
	[ Song Y H, Liu H X, Jiang X Y , et al. Research on scenario evolution of food safety incidents based on knowledge element and Bayesian network[J]. Journal of the China Society for Scientific and Technical Information, 2018,37(7):712-720. ]
[12]	王宁, 谢晓珊, 刘海园 . 基于案例的突发事件推演规则验证方法[J]. 系统工程学报, 2019,34(2):145-157,237.
	[ Wang N, Xie X S, Liu H Y . Validation method of emergency deduction rules based on cases[J]. Journal of Systems Engineering, 2019,34(2):145-157,237. ]
[13]	曾银东 . 福建宁德海洋工程风暴潮灾害风险特征参数分析[J]. 应用海洋学学报, 2017,36(4):500-511.
	[ Zeng Y D . Risk characteristics of storm surge hazards on marine project in Ningde[J]. Journal of Applied Oceanography, 2017,36(4):500-511. ]
[14]	张敏, 罗军, 胡金磊 , 等. 雷州市沿海风暴潮淹没危险性评估[J]. 热带海洋学报, 2019,38(2):1-12.
	[ Zhang M, Luo J, Hu J L , et al. Inundation risk assessment of storm surge along Lei Zhou coastal areas[J]. Journal of Tropical Oceanography, 2019,38(2):1-12. ]
[15]	原楠, 陈新平, 陈学恩 , 等. 罗源湾海域台风风暴潮数值模拟研究[J]. 海洋通报, 2019,38(1):20-30.
	[ Yuan N, Chen X P, Chen X E , et al. Numerical simulation of typhoon storm surge at the Luoyuan Bay[J]. Marine Science Bulletin, 2019,38(1):20-30. ]
[16]	汤富平, 郭见兵, 余华芬 , 等. 基于GIS的台风风暴潮淹没情景模拟方法与平台开发[J]. 地理与地理信息科学, 2019,35(1):6-11,2,19.
	[ Tang F P, Guo J B, Yu H F , et al. Scenario simulation method and platform of typhoon storm surge inundation based on geographic information system[J]. Geography and Geo-information Science, 2019,35(1):6-11,2,19. ]
[17]	Zhu X H, Li X Y, Wang S Y , et al. Scenarios conversion deduction method of natural disaster based on dynamic Bayesian networks[C]. Proceedings of 2017 2 ^nd International Conference on Computational Modeling: Simulation and Applied Mathematics , 2017: 267-271. ]
[18]	孙佳, 左军成, 黄琳 , 等. 东海沿岸台风及风暴潮灾害特征及成因[J]. 河海大学学报(自然科学版), 2013,41(5):461-465.
	[ Sun J, Zuo C J, Huang L , et al. Characteristics and causes of typhoon and storm surge along coast of East China Sea[J]. Journal of Hohai University (Natural Sciences), 2013,41(5):461-465. ]
[19]	范维澄, 闪淳昌 . 公共安全与应急管理[M]. 北京: 科学出版社, 2018.
	[ Fan W C, Shan C C. Public safety and emergency management[M]. Beijing: Science Press, 2018. ]
[20]	李春娟 . 突发事件应急管理知识系统演化研究[D]. 秦皇岛:燕山大学, 2015.
	[ Li C J . Research on the evolution of emergency management knowledge system[D]. Qinhuangdao: Yanshan University, 2015. ]
[21]	冯文娟, 杜云艳, 苏奋振 . 台风时空过程的网络动态分析技术与示例[J]. 地球信息科学学报, 2007,9(5):57-63.
	[ Feng W J, Du Y Y, Su F Z . Dynamic network analyzing technology of typhoon spatio- temporal process and illustration researching[J]. Journal of Geo-information Science, 2007,9(5):57-63. ]
[22]	陈泽强, 陈能成, 杜文英 , 等. 一种洪涝灾害时间信息建模方法[J]. 地球信息科学学报, 2015,17(6):644-652.
	[ Chen Z Q, Chen N C, Du W Y , et al. A method of modelling flood event[J]. Journal of Geo-information Science, 2015,17(6):644-652. ]
[23]	朱晓寒, 李向阳, 王诗莹 . 自然灾害链情景态势组合推演方法[J]. 管理评论, 2016,28(8):143-151.
	[ Zhu X H, Li X Y, Wang S Y . Scenarios combination deduction method of natural disaster[J]. Management Review, 2016,28(8):143-151. ]
[24]	张明红 . 基于案例的非常规突发事件情景推理方法研究[D]. 武汉:华中科技大学, 2012.
	[ Zhang M H . Study on unconventional emergency scenario reasoning method the cases-based[D]. Wuhan: Huazhong University of Science and Technology, 2012. ]
[25]	徐磊 . 基于贝叶斯网络的突发事件应急决策信息分析方法研究[D]. 哈尔滨:哈尔滨工业大学, 2013.
	[ Xu L . Research on emergency events decision making information analysis method based on Bayesian network[D]. Harbin: Harbin Institute of Technology, 2013. ]
[26]	巩前胜 . 基于动态贝叶斯网络的突发事件情景推演模型研究[J]. 西安石油大学学报(自然科学版), 2018,33(2):119-126.
	[ Gong Q S . Research on scenario deduction model of emergency based on dynamic Bayesian network[J]. Journal of Xi'an Shiyou University(Natural Science Edition), 2018,33(2):119-126. ]
[27]	张凯华 . 化工园区火灾事故情景推演及应急知识匹配研究[D]. 青岛:中国石油大学(华东), 2017.
	[ Zhang G H . Research on scenario deduction and emergency knowledge matching of fire accident in chemical industry zone[D]. Qingdao: School of Economics and Management China University of Petroleum (East China), 2017. ]
[28]	杨伟, 李彤 . 非常规灾害事件情景演化的概率性生长模式——基于台风莫拉克的探索性案例研究[J]. 电子科技大学学报(社科版), 2013,15(5):14-19.
	[ Yang W, Li T . Probabilistic growing mode of scenario evolution in unconventionality disaster: Exploratory case study of typhoon Morakot[J]. Journal of University of Electronic Science and Technology of China (Social Sciences Edition), 2013,15(5):14-19. ]

数据	数据来源	描述
承灾体	课题组制定	Shapefile格式;包含沿海城市的堤防工程、重点保护目标、生态敏感目标、沿岸社区人口与房屋、沿岸内陆区域、电力设施、地质灾害高发区等多种类型承灾体的名称、类别、编码、位置、脆弱性等级等
台风数据	气象局	GeoJSON、CSV格式;包含时间、位置、台风等级、台风强度、风速、中心气压、风圈半径等
台风预测数据	中国、中国香港、中国台湾、美国、韩国、日本6个台风预测机构提供	GeoJSON格式
底图	http://www.tianditu.gov.cn/	天地图
应急管理数据	课题组制定	Excel格式;包含名称、类别、编码、数量等
增减水数据	课题组模拟设计	Tiff格式;中国、中国香港、日本3个机构预测的山竹16日17时增减水模拟数据